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One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To develop an automated algorithm to detect, quantify, and differentiate between tremor using pen-on-paper spirals. METHODS: Patients with essential tremor (n = 25), dystonic tremor (n = 25), Parkinson’s disease (n = 25), and healthy volunteers (HV, n = 25) drew free-hand spirals. The algorithm derived the mean deviation (MD) and tremor variability from scanned images. MD and tremor variability were compared with 1) the Bain and Findley scale, 2) the Fahn–Tolosa–Marin tremor rating scale (FTM–TRS), and 3) the peak power and total power of the accelerometer spectra. Inter and intra loop widths were computed to differentiate between the tremor. RESULTS: MD was higher in the tremor group (48.9±26.3) than in HV (26.4±5.3; p < 0.001). The cut-off value of 30.3 had 80.9% sensitivity and 76.0% specificity for the detection of the tremor [area under the curve: 0.83; 95% confidence index (CI): 0.75, 0.91, p < 0.001]. MD correlated with the Bain and Findley ratings (rho = 0.491, p = 0 < 0.001), FTM–TRS part B (rho = 0.260, p = 0.032) and accelerometric measures of postural tremor (total power, rho = 0.366, p < 0.001; peak power, rho = 0.402, p < 0.001). Minimum Detectable Change was 19.9%. Inter loop width distinguished Parkinson’s disease spirals from dystonic tremor (p < 0.001, 95% CI: 54.6, 211.1), essential tremor (p = 0.003, 95% CI: 28.5, 184.9), or HV (p = 0.036, 95% CI: -160.4, -3.9). CONCLUSION: The automated analysis of pen-on-paper spirals generated robust variables to quantify the tremor and putative variables to distinguish them from each other. SIGNIFICANCE: This technique maybe useful for epidemiological surveys and follow-up studies on tremor. [ABSTRACT FROM AUTHOR]

In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches. [ABSTRACT FROM AUTHOR]

Since 2000, there have been more than 8000 publications on radiology artificial intelligence (AI). AI breakthroughs allow complex tasks to be automated and even performed beyond human capabilities. However, the lack of details on the methods and algorithm code undercuts its scientific value. Many science subfields have recently faced a reproducibility crisis, eroding trust in processes and results, and influencing the rise in retractions of scientific papers. For the same reasons, conducting research in deep learning (DL) also requires reproducibility. Although several valuable manuscript checklists for AI in medical imaging exist, they are not focused specifically on reproducibility. In this study, we conducted a systematic review of recently published papers in the field of DL to evaluate if the description of their methodology could allow the reproducibility of their findings. We focused on the Journal of Digital Imaging (JDI), a specialized journal that publishes papers on AI and medical imaging. We used the keyword "Deep Learning" and collected the articles published between January 2020 and January 2022. We screened all the articles and included the ones which reported the development of a DL tool in medical imaging. We extracted the reported details about the dataset, data handling steps, data splitting, model details, and performance metrics of each included article. We found 148 articles. Eighty were included after screening for articles that reported developing a DL model for medical image analysis. Five studies have made their code publicly available, and 35 studies have utilized publicly available datasets. We provided figures to show the ratio and absolute count of reported items from included studies. According to our cross-sectional study, in JDI publications on DL in medical imaging, authors infrequently report the key elements of their study to make it reproducible. [ABSTRACT FROM AUTHOR]

Often it is assumed that electronic recording by observers necessarily provides better quality data than pen-and-paper methods. Fifteen novice observers recorded rates of responding from 10 role-played video samples using one of three continuous recording input formats: keyboard (laptop), touchscreen (personal digital assistants), or pen-and-paper. We evaluated the quality of the observers' data compared with criterion records using calibration and interobserver agreement algorithms. Results of the calibration analysis revealed that observers in the touchscreen group produced the most consistently accurate and precise data, the keyboard group observers showed wide variation in precision and accuracy, and the pen-and-paper group observers were significantly less precise than the touchscreen group. We conclude that although electronic recording has the potential to be as accurate as, and more precise than, pen-and-paper methods, this is far from guaranteed. Analyses of observers' errors advise recommendations for improving data accuracy and precision when using each method. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. [ABSTRACT FROM AUTHOR]

Objectives Comprehensive smokefree laws, as recommended by the WHO Framework Convention of Tobacco Control (WHO FCTC), are the most effective tool to protect the population from secondhand smoke (SHS) and to ensure healthy environments. Studies evaluating how laws govern SHS protection are scarce. This study assessed the level of protection from SHS of laws from countries belonging to the WHO European Region. Methods A new methodology system was developed to evaluate the smokefree legislation according to the principles provided by the WHO guidelines for the correct implementation of Article 8 of the FCTC. For each law, six main sectors and 28 facilities were evaluated. Results Overall 68 laws from 48 countries from the WHO European Region were reviewed. 'Education' and 'Public transport' were the most protected sectors from SHS. Many WHO European laws do not provide protection from SHS across all public sectors. For example, 48.5% of general health facilities and 71.2% of restaurants are unprotected from SHS. The level of protection provided in the 28 facilities studied was low; many WHO European laws still allow smoking under certain conditions, permitting smoking in designated and/or ventilated areas. Conclusions Nine years after the adoption of the WHO FCTC there are still legal formulas in which smoking is allowed in several facilities, through the inclusion of separated areas, ventilated areas and other conditions. Tobacco control efforts still face the challenge of eradicating the legal clauses that prevent 100% smokefree environments. [ABSTRACT FROM AUTHOR]

There are great interests of designing research metrics and indices to measure the research impacts in research institutes. Unfortunately, most of those indices ignore critical design issues, e.g. the disparity between domains, the impact of journals or conferences in which papers are published, normalising the range of the index values to certain intervals, and the scalability of using the index to rank different research entities. In this paper, a new normalised fuzzy index, (NFindex), is proposed as a fuzzy-based research impact metric. The proposed index is a scalable index whose values are normalised to the percentage levels. NFindex achieves both inter-discipline normalisation and intra-discipline consistency. The capability of NFindex to achieve the inter-discipline normalisation enables fair comparison between different research domains regardless their nature in terms of influence and contribution to other research areas, e.g. natural science. Therefore, NFindex gives a universal normalised single-number metric that can be used by research institutes to solve the problem of inter-discipline scholar ranking. Moreover, it can help universal ranking of universities and research institutes according to their research capabilities and impacts. The obtained results, on diverse research areas, prove the potential of NFindex in terms of both intra-discipline consistency and inter-discipline normalisation. [ABSTRACT FROM AUTHOR]

Background The research of alcohol consumption-related problems is a multidisciplinary field. The aim of this study is to analyze the worldwide scientific production in the area of alcohol-drinking and alcohol-related problems from 2005 to 2009. Methods A MEDLINE and Scopus search on alcohol (alcohol-drinking and alcohol-related problems) published from 2005 to 2009 was carried out. Using bibliometric indicators, the distribution of the publications was determined within the journals that publish said articles, specialty of the journal (broad subject terms), article type, language of the publication, and country where the journal is published. Also, authorship characteristics were assessed (collaboration index and number of authors who have published more than 9 documents). The existing research groups were also determined. Results About 24,100 documents on alcohol, published in 3,862 journals, and authored by 69,640 authors were retrieved from MEDLINE and Scopus between the years 2005 and 2009. The collaboration index of the articles was 4.83 ± 3.7. The number of consolidated research groups in the field was identified as 383, with 1,933 authors. Documents on alcohol were published mainly in journals covering the field of ' Substance- Related Disorders,' 23.18%, followed by ' Medicine,' 8.7%, ' Psychiatry,' 6.17%, and ' Gastroenterology,' 5.25%. Conclusions Research on alcohol is a consolidated field, with an average of 4,820 documents published each year between 2005 and 2009 in MEDLINE and Scopus. Alcohol-related publications have a marked multidisciplinary nature. Collaboration was common among alcohol researchers. There is an underrepresentation of alcohol-related publications in languages other than English and from developing countries, in MEDLINE and Scopus databases. [ABSTRACT FROM AUTHOR]

Background: The wide use of the term Cognitive Reserve (CR) is in need of a clear and shared definition of its concept and of the development of new tools, quick and easy to use and updated for the people of today. This study describes the online short CRIq (s-CRIq), the new shorter version of the CRIq, following an item analysis revision, and compares the data distribution of different samples. Methods: The s-CRIq was administered online to 435 people while another 440 filled out the s-CRIq in self-administration. A further 588 participants had been administered the original paper-and-pencil long CRIq and 344 the online long CRIq. Results: The major difference in the databases of s-CRIq versus the long versions is an increased score in education and in leisure activity. However, the density distributions of the total score of CRI in the 4 databases share 64% of their areas, and at least two of them share 84%. Conclusion: The s-CRIq proved to be a simple and easy-to-administer tool. Similarly, to the original version, the s-CRIq is freely available on the web, and it is our hope that it will be of fruitful use for researchers and clinicians alike. [ABSTRACT FROM AUTHOR]

In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly prominent. Therefore, the school has regarded students' mental health education as the top priority in teaching work. Effective expression classification can assist psychology researchers to study psychology and other disciplines and analyze children's psychological activities and mental states by classifying expressions, thereby reducing the occurrence of psychological behavior problems. Most of the current mainstream methods focus on the exploration of text explicit features and the optimization of representation models, and few works pay attention to deeper language expressions. Metaphors, as language expressions often used in daily life, are closely related to an individual's emotion, cognition, and psychological state. This paper studies children's smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students' online patterns from online behavior sequences, calculate abnormal scores based on students' consumption data in the cafeteria, and describe the dietary differences among students. At the same time, this paper uses the students' psychological state data provided by the psychological center as a label to improve the deficiencies caused by the questionnaire. This paper uses the training set to train five common classification algorithms, evaluates them through the validation set, and selects the best classifier as our algorithm and uses it to identify students with mental health problems in the test set. The experimental results show that precision reaches 0.68, recall reaches 0.56, and F 1 -measure reaches 0.67. [ABSTRACT FROM AUTHOR]

Purpose: In the prehospital care of potentially seriously injured patients resource allocation adapted to injury severity (triage) is a challenging. Insufficiently specified triage algorithms lead to the unnecessary activation of a trauma team (over-triage), resulting in ineffective consumption of economic and human resources. A prehospital trauma triage algorithm must reliably identify a patient bleeding or suffering from significant brain injuries. By supplementing the prehospital triage algorithm with in-hospital established point-of-care (POC) tools the sensitivity of the prehospital triage is potentially increased. Possible POC tools are lactate measurement and sonography of the thorax, the abdomen and the vena cava, the sonographic intracranial pressure measurement and the capnometry in the spontaneously breathing patient. The aim of this review was to assess the potential and to determine diagnostic cut-off values of selected instrument-based POC tools and the integration of these findings into a modified ABCDE based triage algorithm. Methods: A systemic search on MEDLINE via PubMed, LIVIVO and Embase was performed for patients in an acute setting on the topic of preclinical use of the selected POC tools to identify critical cranial and peripheral bleeding and the recognition of cerebral trauma sequelae. For the determination of the final cut-off values the selected papers were assessed with the Newcastle–Ottawa scale for determining the risk of bias and according to various quality criteria to subsequently be classified as suitable or unsuitable. PROSPERO Registration: CRD 42022339193. Results: 267 papers were identified as potentially relevant and processed in full text form. 61 papers were selected for the final evaluation, of which 13 papers were decisive for determining the cut-off values. Findings illustrate that a preclinical use of point-of-care diagnostic is possible. These adjuncts can provide additional information about the expected long-term clinical course of patients. Clinical outcomes like mortality, need of emergency surgery, intensive care unit stay etc. were taken into account and a hypothetic cut-off value for trauma team activation could be determined for each adjunct. The cut-off values are as follows: end-expiratory CO2: < 30 mm/hg; sonography thorax + abdomen: abnormality detected; lactate measurement: > 2 mmol/L; optic nerve diameter in sonography: > 4.7 mm. Discussion: A preliminary version of a modified triage algorithm with hypothetic cut-off values for a trauma team activation was created. However, further studies should be conducted to optimize the final cut-off values in the future. Furthermore, studies need to evaluate the practical application of the modified algorithm in terms of feasibility (e.g. duration of application, technique, etc.) and the effects of the new algorithm on over-triage. Limiting factors are the restriction with the search and the heterogeneity between the studies (e.g. varying measurement devices, techniques etc.). [ABSTRACT FROM AUTHOR]

Objective: The consequences of falls in the elderly are severe, ranging from skin abrasion to hip fracture, which is very easy to cause death. Using advanced technology to develop anti-fall clothing that meets the needs of the elderly can play a significant role in protecting the elderly. By reviewing and analyzing the existing literature on the importance of fall protection clothing in reducing falls and protecting the body of the elderly, it is hoped to explore further research that needs improvement. Methods: Guided by the preferred reporting items for systematic reviews and meta-analyses, eight related studies were identified through Web of Science, Scopus and Chinese National Knowledge Infrastructure. The research objects, approaches, material and equipment, protection principle, and survey results are extracted. Results: Two articles verified the fall detection algorithm adopted in the research through experiments, which significantly improved fall detection accuracy. Six papers found that selecting appropriate cushioning materials can effectively reduce the consequences of falls of the elderly through experimental comparative analysis. Finally, three attributes for significant design value are drawn: (1) size and fit; (2) cushioning materials; (3) wearable sensing elements. Conclusions: Anti-fall clothing can effectively protect the elderly when they fall. Further design experiments are needed to select appropriate cushioning materials and wearable sensing elements based on anthropometry and aesthetics to design the style and structure of the garment to achieve the purpose of protecting the elderly. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

Simple Summary: The 2021 WHO classification of central nervous system (CNS) tumors is challenging for neuroradiologists due to the central role of the molecular profile of tumors. We performed a scoping review of recent literature to assess the existing data on the power of novel data analysis tools to predict new tumor classes by imaging. We found room for performance improvement for subgroups with lower incidence (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas) and patients with rare diagnoses (e.g., pediatric gliomas, midline gliomas). More data regarding functional MRI techniques need to be collected. Studies explicitly designed to assess the generalizability of AI-aided tools for predicting molecular tumor subgroups are lacking. The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed. [ABSTRACT FROM AUTHOR]

Background: Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking. Aims: This research proposes an approach for assessing cognitive decline by analysing speech data, specifically focusing on speech relevance as a crucial indicator for memory recall. Methods & Procedures: This is a cross‐sectional, online, self‐administered. The proposed method used deep learning architecture based on transformers, with BERT (Bidirectional Encoder Representations from Transformers) and Sentence‐Transformer to derive encoded representations of speech transcripts. These representations provide contextually descriptive information that is used to analyse the relevance of sentences in their respective contexts. The encoded information is then compared using cosine similarity metrics to measure the relevance of uttered sequences of sentences. The study uses the Pitt Corpus Dementia dataset for experimentation, which consists of speech data from individuals with and without dementia. The accuracy of the proposed multi‐QA‐MPNet (Multi‐Query Maximum Inner Product Search Pretraining) model is compared with other pretrained transformer models of Sentence‐Transformer. Outcomes & Results: The results show that the proposed approach outperforms the other models in capturing context level information, particularly semantic memory. Additionally, the study explores the suitability of different similarity measures to evaluate the relevance of uttered sequences of sentences. The experimentation reveals that cosine similarity is the most appropriate measure for this task. Conclusions & Implications: This finding has significant implications for the early warning signs of dementia, as it suggests that cosine similarity metrics can effectively capture the semantic relevance of spoken language. The persistent cognitive decline over time acts as one of the indicators for prevalence of dementia. Additionally early dementia could be recognised by analysis on other modalities like speech and brain images. WHAT THIS PAPER ADDS: What is already known on this subject: It is already known that speech‐ and language‐based detection methods can be useful for dementia diagnosis, as language difficulties are often early signs of the disease. Additionally, deep learning algorithms have shown promise in detecting and diagnosing dementia through analysing large datasets, particularly in speech‐ and language‐based detection methods. However, further research is needed to validate the performance of these algorithms on larger and more diverse datasets and to address potential biases and limitations. What this paper adds to existing knowledge: This study presents a unique and effective approach for cognitive decline assessment through analysing speech data. The study provides valuable insights into the importance of context and semantic memory in accurately detecting the potential in dementia and demonstrates the applicability of deep learning models for this purpose. The findings of this study have important clinical implications and can inform future research and development in the field of dementia detection and care. What are the potential or actual clinical implications of this work?: The proposed approach for cognitive decline assessment using speech data and deep learning models has significant clinical implications. It has the potential to improve the accuracy and efficiency of dementia diagnosis, leading to earlier detection and more effective treatments, which can improve patient outcomes and quality of life. [ABSTRACT FROM AUTHOR]

Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners' pronunciation errors but also assessing their overall pronunciation level. Method. This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are proposed, respectively. External features are mainly obtained by calculating the hierarchical centrality, node density, and node coverage of nodes in the ontology tree; internal features are mainly obtained by measuring the richness of internal information. According to the similarity of ontology tree and the external features and internal features of nodes, the calculation formula of structural comprehensive similarity is improved, and the features of ontology itself can be fully considered in the calculation. According to the difference of the structure, the weights of the corresponding features during the calculation are adjusted autonomously, so that the calculation results are closer to reality. In spectral image preprocessing, endpoint detection utilizes the harmonic characteristics presented by narrowband spectrograms with high frequency resolution and eliminates useless nonspeech segments by detecting the presence of voiced segments. When building the neural network model, four convolutional layers, two fully connected layers, and one softmax output layer were conceived, and dropout was used to randomly suspend the work of some neurons to avoid overfitting. Results/Discussion. Through the data analysis of mean and variance and verified by one-way analysis of variance, it proves that the sentiment evaluation method in this paper is effective. The traditional multiple linear regression method is not suitable for the corpus and application scenarios of this paper. This paper proposes a decision tree structure, which is similar to the overall scoring process of raters, and uses the Interactive Dicremiser version 3 (ID3) algorithm to build a comprehensive evaluation decision tree for pitch, rhythm, intonation, speech rate, and emotion indicators. It is proved by experiments that the accurate consistency rate of the human-machine evaluation in this paper is 93%, the adjacent consistency rate is 96%, and the Pearson correlation coefficient value of the human-machine evaluation results is 0.89. The data results prove that the evaluation method in this paper is credible. [ABSTRACT FROM AUTHOR]

Background: AI can positively influence teaching by offering support for classroom management, creating inclusive learning environments, enhancing digital skills, personalizing teaching methods, and strengthening teacher‐student relationships. Objectives: This quantitative research study investigates the opportunities, difficulties, and consequences of incorporating AI into teacher education. Methods: Data were collected through structured questionnaires from 202 college students and 68 staff members. The analysis was conducted using SPSS software. Results: The study provides a novel contribution by its thorough investigation of the diverse effects of AI on teacher education. It offers beneficial perspectives on the possible benefits and challenges, illuminating the far‐reaching changes that AI could bring to the terrain of learning and instruction and teaching methods in the time yet to come. The research sought to assess the effect of AI adoption in teacher education across five main dimensions: (i) its influence on teaching support and classroom management, (ii) its role in creating inclusive and accessible learning environments, (iii) its contribution to improving teachers' digital literacy and computer skills, and enhancing access to digital teaching resources, (iv) its positive influence on identifying students' learning styles and facilitating the adoption of diverse teaching methods, and (v) its role in strengthening teacher‐student relationships through improved interactions. Conclusion: The findings elucidate the promising opportunities that AI presents in the field of teacher education, along with the obstacles that require resolution for the effective fusion of AI educational settings. Lay Description: What is currently known about this topic?: AI has the potential to enhance various aspects of teaching, including classroom management and personalizing teaching methods.Incorporating AI into education has garnered significant interest due to its perceived benefits in improving learning outcomes. What does this paper add?: This paper provides a comprehensive investigation into the effects of AI adoption in teacher education, highlighting both the opportunities and challenges associated with its implementation.It offers insights into how AI can influence different dimensions of teaching, such as classroom management, learning environment inclusivity, and teacher‐student relationships. Implications for practice/or policy: The findings of this study underscore the importance of integrating AI into teacher education programs to leverage its potential benefits in enhancing teaching practices.Policymakers and educators should consider the implications of AI adoption in education and develop strategies to address challenges while maximizing the advantages of AI technologies in teaching and learning. [ABSTRACT FROM AUTHOR]

Background: Learner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions. Objectives: Aiming to enable a thorough understanding of trace‐based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self‐reports, which extends conventional statistical methods with psychological networks analysis. Methods: The study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self‐efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis. Results and Conclusions: The inclusion of psychological networks in the analysis of learners' self‐reports collected before the start of the MOOC offers better understanding of trace‐based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self‐reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile. Major Takeaways: The combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow. Lay Description: What is already known about this topic: Researchers have made extensive use of data from online learning platforms—often referred to as learning trace data—to understand how learners engage in online learning activities.Surveys are often used to gather information about learners' motivation, perceptions, and other internal factors, to complement the insight gleaned from learning traces and thus create a more complete picture of the learning behaviour.When analysing survey data, the focus is usually on individual factors without considering how different factors are connected and affect one another.Psychological networks analysis is a novel analytic approach to studying complex phenomena both in psychology and education. What this paper adds: The paper uses psychological networks as a method for examining mutual relations among learners' internal factors (e.g., motivation, perceptions) measured through self‐reports before and after a course.By combining conventional statistical analysis of self‐reports with psychological networks analysis, the paper develops a comprehensive picture of learners' internal factors measured through self‐reports, one that accounts for both individual and interconnected nature of those factors.This comprehensive approach to information extraction from surveys allows for better understanding of trace‐based learner profiles, that is, profiles mined from data about learners' interaction with online learning activities. Implications for practice and/or policy: The presented method paves a way for a comprehensive analysis of the data collected from learners through surveys.It helps us gain a better understanding of learners' engagement with online learning activities.It can advance the evaluation of digital educational programs that aim to encourage changes in health‐related behaviour. [ABSTRACT FROM AUTHOR]

Introduction: Data mining in healthcare is a nascent arena of research in healthcare. Heterogeneity of Diabetes Mellitus in terms of clinical presentation calls for newer methods of research to study potential risk factors. Aim: The paper aims to use clustering techniques to identify the relationship between the four variables, namely the pre-prandial and postprandial sugar level, age and sex. Methods: The data was taken from a diagnostic laboratory in Wagholi, Pune. We conducted K-mean algorithm, EM algorithm, model-based clustering and t-mixture model. Results: It is evidenced that the data was best fitted to the t-mixture model. Our 50% samples were people with diabetes, 17% had prediabetes. Trivial correlation existed between age and sugar level. Males and females were equally at risk of having diabetes. Data presented concludes that age and sex have no effect on the risk of having diabetes. Data mining can be used to deduce meaningful clusters to drive plan-based interventions in the population. Conclusion: Methods of data mining can be used to deduce meaningful clusters in a heterogeneous dataset thus providing policymakers and healthcare researchers with novel information that will potentially contribute in formulating evidence-based policies. [ABSTRACT FROM AUTHOR]

Hypertensive disorders of pregnancy (HDPs) represent a significant source of severe maternal and fetal morbidity. Screening strategies relying on traditional medical history and clinical risk factors have traditionally shown relatively modest performance, mainly in the prediction of preeclampsia, displaying a sensitivity of 37% for the early-onset form and 29% for the late-onset form. The development of more accurate predictive and diagnostic models of preeclampsia in the early stages of pregnancy represents a matter of high priority. The aim of the present paper is to create an effective second trimester prediction algorithm of early-onset HDP occurrence and severity, by combining the following two biochemical markers: a soluble fms-like tyrosine kinase 1 (sFlt-1)/placental growth factor (PlGF) ratio and uterine artery Doppler ultrasound parameters, namely the pulsatility index (PI) and the resistivity index (RI), in a population of high-risk pregnant women, initially assessed through traditional risk factors. A prospective single-center observational longitudinal study was conducted, in which 100 women with singleton pregnancy and traditional clinical and medical history risk factors for preeclampsia were enrolled at 24 weeks of gestation. Shortly after study enrollment, all women had their sFlt-1 and PlGF levels and mean uterine artery PI and RI determined. All pregnancies were followed up until delivery. Receiver operating characteristic (ROC) analysis established algorithms based on cutoffs for the prediction of the later development of preeclampsia: PI 1.25 (96.15% sensitivity, 86.49% specificity), RI 0.62 (84.6% sensitivity, 89.2% specificity) and sFlt-1/PlGF ratio 59.55 (100% sensitivity, 89.2% specificity). The sFlt-1/PlGF ratio was the best predictor for preeclampsia, as it displayed the highest area under the curve (AUC) of 0.973. The prediction algorithm for the severe form of preeclampsia, complicated by fetal growth restriction leading to preterm birth, antepartum fetal demise or acute fetal distress with a cerebro-placental ratio of

We used quantitative text analysis to examine conversations in a series of online support groups attended by care partners of people living with rare dementias (PLWRD). We used transcripts of 14 sessions (>100,000 words) to explore patterns of communication in trained facilitators' (n = 2) and participants' (n = 11) speech and to investigate the impact of session agenda on language use. We investigated the features of their communication via Poisson regression and a clustering algorithm. We also compared their speech with a natural speech corpus. We found that differences to natural speech emerged, notably in emotional tone (d = −3.2, p < 0.001) and cognitive processes (d = 2.8, p < 0.001). We observed further differences between facilitators and participants and between sessions based on agenda. The clustering algorithm categorised participants' contributions into three groups: sharing experience, self-reflection, and group processes. We discuss the findings in the context of Social Comparison Theory. We argue that dedicated online spaces have a positive impact on care partners in combatting isolation and stress via affiliation with peers. We then discuss the linguistic mechanisms by which social support was experienced in the group. The present paper has implications for any services seeking insight into how peer support is designed, delivered, and experienced by participants. [ABSTRACT FROM AUTHOR]

Recent research has shown an indirect effect of generativity on fear of death through ego-integrity in older adults. The present paper aims at demonstrating that the indirect effect is valid even when controlling for social desirability. For that purpose, participants (N = 260 German adults) in study 1 provided self-reports on generativity, ego-integrity, fear of death, and social desirability. Analyses confirmed the indirect effect when the tendency for socially desirable responding was statistically controlled. In study 2, participants (N = 133 German adults) also reported on their generativity and ego-integrity. Fear of death, however, was assessed with a reaction time-based measure (i.e., the Implicit Associations Test). Again, the indirect effect could be confirmed. Taken together, the studies lend further credibility to the extant findings on the indirect effect of generativity on fear of death through ego-integrity. [ABSTRACT FROM AUTHOR]

Background: Polypharmacy in haemodialysis patients can not only manifest as the continued prescribing of unnecessary medications but also has the potential to increase medication‐related hospital admissions, morbidity, and mortality. A validated deprescribing algorithm has recently been developed, specifically targeted at dialysis patients. This paper audits the experience of implementing this algorithm in Australian dialysis settings. Aim: The aim of this paper was to evaluate the usefulness and applicability of the pharmacist‐led Toronto deprescribing tool in Australian haemodialysis settings. Method: The pharmacist‐led deprescribing algorithm was implemented across two metropolitan sites and one rural site. The audit focused on five medications that could potentially be deprescribed in the target patient group (diuretics, alpha blockers, statins, proton pump inhibitors [PPIs], and quinine). Between 1 and 12 months later, a reaudit was conducted, with patients followed up to confirm if medications remained deprescribed. Results: Two hundred and eleven patients across three sites were reviewed. Application of the algorithm resulted in 168 medications deemed appropriate to deprescribe. Of these 168 medications, 56 (33%) were initially deprescribed, with 50 medications (30%) remaining deprescribed on reaudit. The deprescribing rates varied between the three different services, with initial deprescribing rates ranging from 18% to 61%. After follow‐up, deprescribing changes across target medications were fairly static, with only a small number of patients restarting either their diuretic or PPI. Conclusion: The pharmacist‐led deprescribing algorithm resulted in substantial deprescribing across the three sites. Deprescribing rates varied between the sites due to differences in the team model that the pharmacist worked within and the method of the rollout. Further studies should draw on aspects such as finding enablers to overcome prescriber and patient concerns about deprescribing and the aspects of which team models lead to higher rates of successful deprescribing. [ABSTRACT FROM AUTHOR]

Predicting the polyproline type II (PPII) helix structure is crucial important in many research areas, such as the protein folding mechanisms, the drug targets, and the protein functions. However, many existing PPII helix prediction algorithms encode the protein sequence information in a single way, which causes the insufficient learning of protein sequence feature information. To improve the protein sequence encoding performance, this paper proposes a BERT-based PPII helix structure prediction algorithm (BERT-PPII), which learns the protein sequence information based on the BERT model. The BERT model's CLS vector can fairly fuse sample's each amino acid residue information. Thus, we utilize the CLS vector as the global feature to represent the sample's global contextual information. As the interactions among the protein chains' local amino acid residues have an important influence on the formation of PPII helix, we utilize the CNN to extract local amino acid residues' features which can further enhance the information expression of protein sequence samples. In this paper, we fuse the CLS vectors with CNN local features to improve the performance of predicting PPII structure. Compared to the state-of-the-art PPIIPRED method, the experimental results on the unbalanced dataset show that the proposed method improves the accuracy value by 1% on the strict dataset and 2% on the less strict dataset. Correspondingly, the results on the balanced dataset show that the AUCs of the proposed method are 0.826 on the strict dataset and 0.785 on less strict datasets, respectively. For the independent test set, the proposed method has the AUC value of 0.827 on the strict dataset and 0.783 on the less strict dataset. The above experimental results have proved that the proposed BERT-PPII method can achieve a superior performance of predicting the PPII helix. [ABSTRACT FROM AUTHOR]

(1) Introduction: Psoriasis is a chronic, immune-mediated disease that negatively impacts patients' quality of life and predisposes them to cardiovascular or metabolic diseases. This paper aims to summarize the knowledge structure and future directions in psoriasis research by means of bibliometrics. (2) Material and methods: The Thomson Reuters Web of Science database was interrogated using preestablished keywords. A list of the top 100 most cited articles focusing solely on psoriasis was compiled and analyzed. VOSviewer software was used to assess and visualize collaboration networks, citation, co-citation and co-wording analysis, and bibliographic coupling. (3) Results: The articles were written by 902 authors from 20 countries and were published in 31 journals. The United States was at the forefront of this field. Griffiths, CEM had the most citations, while the most prolific institution was Rockefeller University, New York City. Pathogenesis, especially key-pathogenic factors, immune pathways, and epidemiology were the most discussed topics. Work published in the last decade focused on the use of biologics. Keywords such as "quality of life", "efficacy", and "necrosis-factor alpha" have been widely used. (4) Conclusion: Research interest regarding psoriasis is high, leading to the rapid development of this field. Treatment modalities, especially novel-targeted therapies, immune pathways, and an integrative approach to such cases are receiving great interest and represent research hotspots in the future. [ABSTRACT FROM AUTHOR]

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods. [ABSTRACT FROM AUTHOR]

With the continuous updating and advancement of artificial intelligence technology, it gradually begins to shine in various industries, especially playing an increasingly important role in incremental music teaching and assisted therapy systems. This study designs artificial intelligence models from the perspectives of attention mechanism, contextual information guidance, and distant dependencies combined with incremental music teaching for the segmentation of MS (multiple sclerosis) lesions and achieves the automatic and accurate segmentation of MS lesions through the multidimensional analysis of multimodal magnetic resonance imaging data, which provides a basis for physicians to quantitatively analyze MS lesions, thus assisting them in the diagnosis and treatment of MS. To address the highly variable characteristics of MS lesion location, size, number, and shape, this paper firstly designs a 3D context-guided module based on Kronecker convolution to integrate lesion information from different fields of view, starting from lesion contextual information capture. Then, a 3D spatial attention module is introduced to enhance the representation of lesion features in MRI images. The experiments in this paper confirm that the context-guided module, cross-dimensional cross-attention module, and multidimensional feature similarity module designed for the characteristics of MS lesions are effective, and the proposed attentional context U-Net and multidimensional cross-attention U-Net have greater advantages in the objective evaluation index of lesion segmentation, while being combined with the incremental music teaching approach to assist treatment, which provides a new idea for the intelligent assisted treatment approach. In this paper, from algorithm design to experimental validation, both in terms of accuracy, the operational difficulty of the experiment, consumption of arithmetic power, and time cost, the unique superiority of the artificial intelligence attention-based combined with incremental music teaching adjunctive therapy system proposed in this paper can be seen in the MS lesion segmentation task. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common cause of dementia. Accurate prediction and diagnosis of AD and its prodromal stage, i.e., mild cognitive impairment (MCI), is essential for the possible delay and early treatment for the disease. In this paper, we adopt the data from the China Longitudinal Aging Study (CLAS), which was launched in 2011, and includes a joint effort of 15 institutions all over the country. Four thousand four hundred and eleven people who are at least 60 years old participated in the project, where 3,514 people completed the baseline survey. The survey collected data including demographic information, daily lifestyle, medical history, and routine physical examination. In particular, we employ ensemble learning and feature selection methods to develop an explainable prediction model for AD and MCI. Five feature selection methods and nine machine learning classifiers are applied for comparison to find the most dominant features on AD/MCI prediction. The resulting model achieves accuracy of 89.2%, sensitivity of 87.7%, and specificity of 90.7% for MCI prediction, and accuracy of 99.2%, sensitivity of 99.7%, and specificity of 98.7% for AD prediction. We further utilize the SHapley Additive exPlanations (SHAP) algorithm to visualize the specific contribution of each feature to AD/MCI prediction at both global and individual levels. Consequently, our model not only provides the prediction outcome, but also helps to understand the relationship between lifestyle/physical disease history and cognitive function, and enables clinicians to make appropriate recommendations for the elderly. Therefore, our approach provides a new perspective for the design of a computer-aided diagnosis system for AD and MCI, and has potential high clinical application value. [ABSTRACT FROM AUTHOR]

In recent times, there has been considerable focus on harnessing artificial intelligence (AI) for medical image analysis and healthcare purposes. In this study, we introduce CADFU (Computer-Aided Diagnosis System for Foot Ulcers), a pioneering diabetic foot ulcer diagnosis system. The primary objective of CADFU is to detect and segment ulcers and similar chronic wounds in medical images. To achieve this, we employ two distinct algorithms. Firstly, DHuNeT, an innovative Dual-Phase Hyperactive UNet, is utilized for the segmentation task. Second, we used YOLOv8 for the task of detecting wounds. The DHuNeT autoencoder, employed for the wound segmentation task, is the paper's primary and most significant contribution. DHuNeT is the combination of sequentially stacking two UNet autoencoders. The hyperactive information transmission from the first UNet to the second UNet is the key idea of DHuNeT. The first UNet feeds the second UNet the features it has learned, and the two UNets combine their learned features to create new, more accurate, and effective features. We achieve good performance measures, especially in terms of the Dice co-efficient and precision, with segmentation scores of 85% and 92.6%, respectively. We obtain a mean average precision (mAP) of 86% in the detection task. Future hospitals could quickly monitor patients' health using the proposed CADFU system, which would be beneficial for both patients and doctors. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper proposes a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN) and implemented on three publicly available benchmark datasets (ISIC 2020, HAM10000, and DermIS). The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17% accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The accuracies of ResNet-152, Vgg-19, MobileNet, and Vgg-16, EfficientNet-B0, and Inception-V3 are 89.68%, 92.51%, 91.46%, 89.12%, 89.46%, and 91.82%, respectively. The results showed that the proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer. Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer. [ABSTRACT FROM AUTHOR]

Face recognition segmentation is very important for symptom detection, especially in the case of complex image backgrounds or noise. The complexity of the photo background, the clarity of the facial expressions, or the interference of other people's faces can increase the difficulty of detection. Therefore, in this paper, we have proposed a method to combine mask region-based convolutional neural networks (Mask R-CNN) with you only look once version 4 (YOLOv4) to identify facial symptoms by this new method. We use the face image dataset from the public image databases DermNet and Freepic as the training source for the model. Face segmentation was first applied with Mask R-CNN. Then the images were imported into ResNet-101, and the facial features were fused with region of interest (RoI) in the feature pyramid networks (FPN) structures. After removing the non-face features and noise, the face region has been accurately obtained. Next, the recognized face area and RoI data were used to identify facial symptoms (acne, freckle, and wrinkles) with YOLOv4. Finally, we use Mask R-CNN, and you only look once version 3 (YOLOv3) and YOLOv4 are matched to perform the performance analysis. Although, the facial images with symptoms are relatively few. We still use a limited amount of data to train the model. The experimental results show that our proposed method still achieves 57.73%, 60.38%, and 59.75% of mean average precision (mAP) for different amounts of data. Compared with other methods, the mAP was more than about 3%. Consequently, using the method proposed in this paper, facial symptoms can be effectively and accurately identified. [ABSTRACT FROM AUTHOR]

Simple Summary: According to cancer statistics published in 2020, there were 19.3 million new cancer cases and almost 10.0 million cancer deaths worldwide. This suggests that cancer is one of the main health threats worldwide. Survival analysis, combining the exponential distribution with regression analysis, can predict the time when a specific event will occur. This nationwide follow-up study aims to present survival deep learning models to assist in the early identification of cancer incidence. Our models consistently achieved high performance in 10 types of cancer. By applying the techniques outlined in this paper, clinical biomarkers, demographic, and anthropometric data can be utilized to predict the risk of cancer occurrence. Background: Cancer is one of the main global health threats. Early personalized prediction of cancer incidence is crucial for the population at risk. This study introduces a novel cancer prediction model based on modern recurrent survival deep learning algorithms. Methods: The study includes 160,407 participants from the blood-based cohort of the Korea Cancer Prevention Research-II Biobank, which has been ongoing since 2004. Data linkages were designed to ensure anonymity, and data collection was carried out through nationwide medical examinations. Predictive performance on ten cancer sites, evaluated using the concordance index (c-index), was compared among nDeep and its multitask variation, Cox proportional hazard (PH) regression, DeepSurv, and DeepHit. Results: Our models consistently achieved a c-index of over 0.8 for all ten cancers, with a peak of 0.8922 for lung cancer. They outperformed Cox PH regression and other survival deep neural networks. Conclusion: This study presents a survival deep learning model that demonstrates the highest predictive performance on censored health dataset, to the best of our knowledge. In the future, we plan to investigate the causal relationship between explanatory variables and cancer to reduce cancer incidence and mortality. [ABSTRACT FROM AUTHOR]

Simple Summary: Melanoma is the most dangerous type of skin cancer worldwide. Early detection of melanoma is crucial for better outcomes, but this often can be challenging. This research explores the use of artificial intelligence (AI) techniques combined with non-invasive imaging methods to improve melanoma detection. The authors aim to evaluate the current state of AI-based techniques using tools including dermoscopy, optical coherence tomography (OCT), and reflectance confocal microscopy (RCM). The findings demonstrate that several AI algorithms perform as well as or better than dermatologists in detecting melanoma, particularly in the analysis of dermoscopy images. This research highlights the potential of AI to enhance diagnostic accuracy, leading to improved patient outcomes. Further studies are needed to address limitations and ensure the reliability and effectiveness of AI-based techniques. Background: Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. Objective: The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. Methods: A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. Results: We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. Conclusions: Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability. [ABSTRACT FROM AUTHOR]

Introduction: Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently. Methods: In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance. Results: The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus. Conclusion: Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subject. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper presents a cervical cancer detection approach where the KNN Imputer techniques is used to fill the missing values and after that SMOTE upsampled features are utilized to train a multi-model ensemble learning approach. Results demonstrate that use of KNN Imputed SMOTE features yields better results than the original features to classify cancerous and normal patients. Objective: Cervical cancer ranks among the top causes of death among females in developing countries. The most important procedures that should be followed to guarantee the minimizing of cervical cancer's aftereffects are early identification and treatment under the finest medical guidance. One of the best methods to find this sort of malignancy is by looking at a Pap smear image. For automated detection of cervical cancer, the available datasets often have missing values, which can significantly affect the performance of machine learning models. Methods: To address these challenges, this study proposes an automated system for predicting cervical cancer that efficiently handles missing values with SMOTE features to achieve high accuracy. The proposed system employs a stacked ensemble voting classifier model that combines three machine learning models, along with KNN Imputer and SMOTE up-sampled features for handling missing values. Results: The proposed model achieves 99.99% accuracy, 99.99% precision, 99.99% recall, and 99.99% F1 score when using KNN imputed SMOTE features. The study compares the performance of the proposed model with multiple other machine learning algorithms under four scenarios: with missing values removed, with KNN imputation, with SMOTE features, and with KNN imputed SMOTE features. The study validates the efficacy of the proposed model against existing state-of-the-art approaches. Conclusions: This study investigates the issue of missing values and class imbalance in the data collected for cervical cancer detection and might aid medical practitioners in timely detection and providing cervical cancer patients with better care. [ABSTRACT FROM AUTHOR]

The present paper introduces a general multidimensional model to measure individual differences in learning within a single administration of a test. Learning is assumed to result from practicing the operations involved in solving the items. The model accounts for the possibility that the ability to learn may manifest differently for correct and incorrect responses, which allows for distinguishing different types of learning effects in the data. Model estimation and evaluation is based on a Bayesian framework. A simulation study is presented that examines the performance of the estimation and evaluation methods. The results show accuracy in parameter recovery as well as good performance in model evaluation and selection. An empirical study illustrates the applicability of the model to data from a logical ability test. [ABSTRACT FROM AUTHOR]

Tissue phenotyping is a fundamental step in computational pathology for the analysis of tumor micro-environment in whole slide images (WSIs). Automatic tissue phenotyping in whole slide images (WSIs) of colorectal cancer (CRC) assists pathologists in better cancer grading and prognostication. In this paper, we propose a novel algorithm for the identification of distinct tissue components in colon cancer histology images by blending a comprehensive learning system with deep features extraction in the current work. Firstly, we extracted the features from the pre-trained VGG19 network which are then transformed into mapped features space for nodes enhancement generation. Utilizing both mapped features and enhancement nodes, the proposed algorithm classifies seven distinct tissue components including stroma, tumor, complex stroma, necrotic, normal benign, lymphocytes, and smooth muscle. To validate our proposed model, the experiments are performed on two publicly available colorectal cancer histology datasets. We showcase that our approach achieves a remarkable performance boost surpassing existing state-of-the-art methods by (1.3% AvTP, 2% F1) and (7% AvTP, 6% F1) on CRCD-1, and CRCD-2, respectively. [ABSTRACT FROM AUTHOR]

In this paper, in-depth research and analysis of juvenile delinquency prevention and occupational therapy education guidance using artificial intelligence are conducted, and its response mechanism is designed in this way. Two crime type prediction algorithms based on time-crime type count vectorization and dense neural network and crime type prediction based on the fusion of dense neural network and long- and short-term memory neural network are proposed. The outputs of both are fed into a new neural network for training to achieve the fusion of the two neural networks. Among them, the use of the dense neural network can effectively fit the relationship between the constructed features and crime types. The behavioral manifestations and causes of the formation of deviant behavior in adolescents are discussed. They can only read numerical data, but there is a lot of information in the textual data that is closely related to the training effect. When experimenting, it is necessary to extract knowledge and build applications. The practical work with adolescents with deviant behaviors is again carried out from group work and casework, respectively, with problem diagnosis, needs assessment, and service plan development for specific clients, to carry out relevant practical service work. The causes of juvenile delinquency in the Internet culture are discussed in terms of the Internet environment, juvenile use of the Internet, Internet supervision, and crime prevention education, respectively. The fourth chapter focuses on the analysis of the prevention and control measures for juvenile delinquency in cyberculture. In response to the above-mentioned causes of juvenile delinquency in cyberculture, the prevention and control measures are discussed in four aspects, namely, strengthening the construction of cyberculture and building a healthy cyber environment, strengthening the capacity building of guiding juveniles to use cyber correctly, building a prevention and supervision system to promote the improvement of the legal system, and improving and innovating the crime prevention education in the cyber era. [ABSTRACT FROM AUTHOR]

To summarise the range and nature of decision-making aids that guide upper limb (UL) neurorehabilitation. Our scoping review followed Arksey and O'Malley's 6-step framework. Electronic databases were systematically searched; grey literature was hand-searched. Included papers were: (1) published in English; (2) related to UL rehabilitation for people with upper motor neuron conditions; and (3) provided a description of, or research on, a decision-making aid that guides therapists when choosing between two or more intervention approaches, techniques, or applications of a technique. Levels of evidence were rated. An expert panel of occupational therapists working in UL neurorehabilitation was consulted. The 24 included articles described 15 decision-making aids with varying breadth and depth of the assessment process and suggested interventions. Six aids had published research, but lacked high quality evidence. The expert panel identified four key decision-making aids as being clinically useful. Preferred aids either included client-centred goal-setting within a holistic assessment or matched specific intervention options to distinct UL assessment results. Prompts to re-evaluate client performance are desirable. Few decision-making aids guide therapists to holistically assess and make specific intervention decisions across all domains of UL neurorehabilitation. Their usefulness depends on the purpose, setting, and therapist experience. Considering the complexity and heterogeneity of upper limb (UL) neurorehabilitation, a selection of evidence-based and purpose-designed decision-making aids may assist therapists across different experience levels and practice settings to choose individualised interventions. Decision-making aids for UL interventions in neurorehabilitation may be more clinically useful if they include a holistic and client-centred information gathering process that focuses on daily life goals. Decision-making aids should illustrate a clear clinical picture based on UL assessment results, with corresponding UL intervention recommendations provided. Inclusion of a cyclic process to re-evaluate client performance and function could also enhance the usefulness of a decision-making aid. [ABSTRACT FROM AUTHOR]

Background: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST‐Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. Methods: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID‐19 and had a length of stay of ≤ 30 days. Results: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST‐Plus‐assisted RD evaluations. The lag between admission and diagnosis improved with MUST‐Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre‐/post‐implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. Conclusion: MUST‐Plus, a machine learning‐based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning‐based processes to improve malnutrition screening and facilitate timely intervention. Key points/Highlights: Malnutrition is prevalent among hospitalised patients and frequently goes unrecognised, with the potential for severe sequelae. Accurate diagnosis, documentation and treatment of malnutrition have the potential of having a positive impact on morbidity rate, mortality rate, length of inpatient stay, readmission rate and hospital revenue. The tool's successful application highlights its potential to optimise malnutrition screening in healthcare systems, offering potential benefits for patient outcomes and hospital finances. [ABSTRACT FROM AUTHOR]

With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]

The university cafeteria is a crucial element in shaping students' dining experiences and their physical well-being. This study was conducted at a public university in Bangladesh. This research has a dual purpose: firstly, to assess student satisfaction with the university cafeteria using key indicators and secondly, to identify critical factors influencing this satisfaction through advanced data analysis employing recursive feature elimination (RFE). The ultimate goal is to enhance cafeteria services, align them with student preferences, and boost overall satisfaction. Data collection involved 150 paper-based questionnaires with mixed data types, including questions based on prior research assessments. The model-building process included the chi-squared test and the recursive feature elimination process. The study identifies 12 factors significantly affecting cafeteria food satisfaction, with four factors having a notable impact: meal type and freshness, staff attitude, and wait length. This research highlights the interconnectedness of seemingly distinct cafeteria factors and their potential to influence health outcomes. Practical recommendations are made for improving cafeteria efficiency and adapting to changing student needs. Health education interventions aimed at enhancing the cafeteria experience for university students should include a quantitative analysis of factors impacting students' overall cafeteria satisfaction. [ABSTRACT FROM AUTHOR]

With the impact of the COVID-19 pandemic, the number of patients suffering from depression is rising around the world. It is important to diagnose depression early so that it may be treated as soon as possible. The self-response questionnaire, which has been used to diagnose depression in hospitals, is impractical since it requires active patient engagement. Therefore, it is vital to have a system that predicts depression automatically and recommends treatment. In this paper, we propose a smartphone-based depression prediction system. In addition, we propose depressive features based on multimodal sensor data for predicting depressive mood. The multimodal depressive features were designed based on depression symptoms defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed system comprises a "Mental Health Protector" application that collects data from smartphones and a big data-based cloud platform that processes large amounts of data. We recruited 106 mental patients and collected smartphone sensor data and self-reported questionnaires from their smartphones using the proposed system. Finally, we evaluated the performance of the proposed system's prediction of depression. As the test dataset, 27 out of 106 participants were selected randomly. The proposed system showed 76.92% on an f1-score for 16 patients with depression disease, and in particular, 15 patients, 93.75%, were successfully predicted. Unlike previous studies, the proposed method has high adaptability in that it uses only smartphones and has a distinction of evaluating prediction accuracy based on the diagnosis. [ABSTRACT FROM AUTHOR]

The main semantic symbol systems for people to express their emotions include natural language and music. The analysis and establishment of semantic association between language and music is helpful to provide more accurate retrieval and recommendation services for text and music. Existing researches mainly focus on the surface symbolic features and association of natural language and music, which limits the performance and interpretability of applications based on semantic association of natural language and music. Emotion is the main meaning of music expression, and the semantic range of text expression includes emotion. In this paper, the semantic features of music are extracted from audio features, and the semantic matching model of audio emotion analysis is constructed to analyze ethnic music audio emotion through feature extraction ability of deep structure. The model is based on the framework of emotional semantic matching technology and realizes the emotional semantic matching of music fragments and words through semantic emotional recognition algorithm. Multiple experiments show that when W = 0.65 , the recognition rate of multichannel fusion model is 88.42%, and the model can reasonably realize audio emotion analysis. When the spatial dimension of music data changes, the classification accuracy reaches the highest when the spatial dimension is 25. Analysing the semantic association of audio promotes the application of folk music in occupational therapy. [ABSTRACT FROM AUTHOR]

This paper adopts the method of psychological data analysis to conduct in-depth research and analysis on the correlation between teachers' classroom teaching behaviors and students' knowledge acceptance. Firstly, this paper proposes a health factor prediction model, which is specifically divided into clustering and then classification model and a clustering and classification synthesis model. The classroom learning process is coded, sampled, and quantified to obtain data on students' learning behaviors, and a visualization system based on classroom students' learning behaviors is designed and developed to record and analyze students' behaviors in the classroom learning process and grasp students' classroom learning. These two models use algorithms to fine-grained divide the dataset from the perspective of subject users and mental health factors, respectively, and then use decision tree algorithms to classify and predict the mental health factor information by the subject user base information. Second, based on the collected datasets, we designed comparison experiments to validate the clustering-then-classification model and the integrated clustering-classification model and selected the optimal model for comparison. Teachers should increase effective praise and encouragement behaviors; teachers should increase meaningful teacher-student interaction behaviors; teachers should be proficient in teaching media technology to reduce unnecessary time wastage. Strategies to enhance teachers' TPACK include enriching teachers' knowledge base of CK, TK, and PK; developing teachers' integration thinking; and enriching teachers' types of activities for integrating technology. [ABSTRACT FROM AUTHOR]

This paper constructs an algorithm for youth school violence recognition and an occupational therapy education model for victims through the extraction of action speech features. For the characteristics of violent actions and daily actions, action features in time and frequency domains are extracted and action categories are recognized by BP neural network; for complex actions, it is proposed to decompose complex actions into basic actions to improve the recognition rate; then, LDA dimensionality reduction algorithm is introduced for the problem of the high complexity of algorithm due to high dimensionality of features, and the feature dimensionality is reduced to 8 dimensions by LDA dimensionality reduction algorithm, which reduces the system running time by about 51% and improves the accuracy of violent action recognition by 3.3% while ensuring the overall performance of the system. The LDA dimensionality reduction algorithm reduces the number of features to 8 dimensions, which reduces the running time of the system by 51%, increases the accuracy rate of violent action recognition by 3.3%, and increases the recall rate of violent action recognition by 8.86% while ensuring the overall performance of the system. Based on the classical D-S theory, we proposed an improved D-S evidence fusion algorithm by modifying the original evidence model with a new probability distribution function and constructing new fusion rules, which can solve the fusion conflict problem well. The recall rate for violent actions is increased to 90.0%, thus reducing the missed alarm rate of the system. [ABSTRACT FROM AUTHOR]