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The advance of machine learning (ML) systems in real‐world scenarios usually expects safe deployment in high‐stake applications (e.g., medical diagnosis) for critical decision‐making process. To this end, provable robustness of ML is usually required to measure and understand how reliable the deployed ML system is and how trustworthy their predictions can be. Many studies have been done to enhance the robustness in recent years from different angles, such as variance‐regularized robust objective functions and conformal prediction (CP) for uncertainty quantification on testing data. Although these tools provably improve the robustness of ML model, there is still an inevitable gap to integrate them into an end‐to‐end deployment. For example, robust objectives usually require carefully designed optimization algorithms, while CP treats ML models as black boxes. This paper is a brief introduction to our recent research focusing on filling this gap. Specifically, for learning robust objectives, we designed sample‐efficient stochastic optimization algorithms that achieves the optimal (or faster compared to existing algorithms) convergence rates. Moreover, for CP‐based uncertainty quantification, we established a framework to analyze the expected prediction set size (smaller size means more efficiency) of CP methods in both standard and adversarial settings. This paper elaborates the key challenges and our exploration towards efficient algorithms with details of background methods, notions for robustness measure, concepts of algorithmic efficiency, our proposed algorithms and results. All of them further motivate our future research on risk‐aware ML that can be critical for AI–human collaborative systems. The future work mainly targets designing conformal robust objectives and their efficient optimization algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Depression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices. Patients and Methods: We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words. Results: Using four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642– 0.895) and 0.85 (95% CI = 0.780– 0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273– 67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented. Conclusion: Through various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis. [ABSTRACT FROM AUTHOR]

In sports, advance sensing technologies generate massive amount of unstructured telemedicine data that need to be refined for accurate diagnosis of underlying diseases. For accurate prediction of diseases and classification of athletes' data, deep learning algorithms are frequently used at the cloud. However, the transmission of raw data of athletes to the cloud faces numerous challenges. Among them, security and privacy are a major challenge in view of the sensitive and personal information present within the unstructured data. In this paper, first we present a data block scrambling algorithm (without key management) for secured transmission and storage of ECG (electrocardiogram) data of table tennis players at the cloud. A small piece of original data stored at the cloud is used for scrambling the massive amount of remaining ECG data. The secured telemedicine data is then imported into Hadoop Distributed File System for data management, which is read by Spark framework to form Resilient Distributed Datasets. Finally, a deep learning approach is used that extracts useful features, learns the related information, and weights and sums the feature vectors at different layers for classification. Theoretical analysis proves that our proposed approach is highly robust and resilient to brute force attacks and at the same time has a much better accuracy, sensitivity, and specificity as compared to the existing approaches. [ABSTRACT FROM AUTHOR]

Conventional methodology in the field for the sampling of coffee leaf rust, caused by Hemileia vastatrix, has proven to be impractical. This paper proposes a method for the early detection of this disease, which is the most significant pathogen of coffee plants worldwide, using multispectral images acquired using a Mapir Survey3W camera and an unmanned aerial vehicle (UAV). For this purpose, 160 coffee seedlings of the coffee cultivar 'Mundo Novo' were inoculated with urediniospores of H. vastatrix and compared with 160 control (non-inoculated) seedlings to determine the most favorable interval for distinguishing healthy and infected plants. The 320 seedlings were placed on a dark surface to perform the imaging flights. In vitro analyses of the physiological parameters of 20 specimens were then performed for each condition (inoculated/non-inoculated) to obtain the hyperspectral curves, and this process was repeated three times at 15, 30, and 45 days after inoculation (DAI). Based on the simulated hyperspectral curves, a discrepancy between the red and near-infrared (NIR) bands was identified at 15 DAI, with the inoculated plants showing greater absorption in the red band and a greater spectral response in the NIR band. Thus, multispectral images were able to distinguish H. vastatrix infection in coffee seedlings at an asymptomatic stage (15 DAI) using a support vector machines (SVM) algorithm. Detection accuracy was 80% and the Kappa index of agreement was moderate (0.6). The early detection of this pathogen in the field using low-cost technology can be an important tool for the monitoring of coffee leaf rust and, consequently, a more sustainable management of the pathogen, causing farmers to make applications of chemical fungicides only when necessary. [ABSTRACT FROM AUTHOR]

Background: As the use of AI becomes more pervasive, and computerised systems are used in clinical decision-making, the role of trust in, and the trustworthiness of, AI tools will need to be addressed. Using the case of computational phenotyping to support the diagnosis of rare disease in dysmorphology, this paper explores under what conditions we could place trust in medical AI tools, which employ machine learning.Methods: Semi-structured qualitative interviews (n = 20) with stakeholders (clinical geneticists, data scientists, bioinformaticians, industry and patient support group spokespersons) who design and/or work with computational phenotyping (CP) systems. The method of constant comparison was used to analyse the interview data.Results: Interviewees emphasized the importance of establishing trust in the use of CP technology in identifying rare diseases. Trust was formulated in two interrelated ways in these data. First, interviewees talked about the importance of using CP tools within the context of a trust relationship; arguing that patients will need to trust clinicians who use AI tools and that clinicians will need to trust AI developers, if they are to adopt this technology. Second, they described a need to establish trust in the technology itself, or in the knowledge it provides-epistemic trust. Interviewees suggested CP tools used for the diagnosis of rare diseases might be perceived as more trustworthy if the user is able to vouchsafe for the technology's reliability and accuracy and the person using/developing them is trusted.Conclusion: This study suggests we need to take deliberate and meticulous steps to design reliable or confidence-worthy AI systems for use in healthcare. In addition, we need to devise reliable or confidence-worthy processes that would give rise to reliable systems; these could take the form of RCTs and/or systems of accountability transparency and responsibility that would signify the epistemic trustworthiness of these tools. words 294. [ABSTRACT FROM AUTHOR]

Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment. Conclusions: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment. [ABSTRACT FROM AUTHOR]

For some years, we have been witnessing a steady stream of high‐profile studies about machine learning (ML) algorithms achieving high diagnostic accuracy in the analysis of medical images. That said, facilitating successful collaboration between ML algorithms and clinicians proves to be a recalcitrant problem that may exacerbate ethical problems in clinical medicine. In this paper, we consider different epistemic and normative factors that may lead to algorithmic overreliance within clinical decision‐making. These factors are false expectations, the miscalibration of uncertainties, non‐explainability, and the socio‐technical context within which the algorithms are utilized. Moreover, we identify different desiderata for bridging the gap between ML algorithms and clinicians. Further, we argue that there is an intriguing dialectic in the collaboration between clinicians and ML algorithms. While it is the algorithm that is supposed to assist the clinician in diagnostic tasks, successful collaboration will also depend on adjustments on the side of the clinician. [ABSTRACT FROM AUTHOR]

Objectives.To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. Methods. We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based risk assessment. We compared different models and determined the strongest predictors of adverse birth outcomes using positive predictive value as the metric for selection. Results. Machine learning algorithms performed similarly, outperforming the current paper-based risk assessment by up to 36%; a refined paper-based assessment outperformed the current assessment by up to 22%. We estimate that these improvements will allow 100 to 170 additional high-risk pregnant women screened for program eligibility each year to receive services that would have otherwise been unobtainable. Conclusions. Our analysis exhibits the potential for machine learning to move government agencies toward a more data-informed approach to evaluating risk and providing social services. Overall, such efforts will improve the efficiency of allocating resource-intensive interventions. [ABSTRACT FROM AUTHOR]

Introduction: Pain is the unpleasant sensation and emotional experience that leads to poor quality of life for millions of people worldwide. Considering the complexity in understanding the principles of pain and its significant impact on individuals and society, research focuses to deliver innovative pain relief methods and techniques. This review explores the clinical uses of machine learning (ML) for the diagnosis, classification, and management of pain. Methods: A systematic review of the current literature was conducted using the PubMed database library. Results: Twenty-six papers related to pain and ML research were included. Most of the studies used ML for effectively classifying the patients' level of pain, followed by use of ML for the prediction of manifestation of pain and for pain management. A less common reason for performing ML analysis was for the diagnosis of pain. The different approaches are thoroughly discussed. Conclusion: ML is increasingly used in pain medicine and appears to be more effective compared to traditional statistical approaches in the diagnosis, classification, and management of pain. [ABSTRACT FROM AUTHOR]

Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review. [ABSTRACT FROM AUTHOR]

To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms were used for upper limb and reaching analyses. The inertial measurement unit technique was applied in studies where the functional status was between mild and severe. The fuzzy logic technique was used for activity classifiers. The prevailing research themes demonstrated the growing utility of AI algorithms for stroke evaluation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We conduct numerical analyses to study the bias and efficiency of these estimators. Furthermore, we compare 2 different model selection strategies based on 1) Akaike's Information Criterion and the Bayesian Information Criterion and 2) machine learning algorithms, and we illustrate double-robust estimators' performance in a real-world setting. In simulations with correctly specified models and near-positivity violations, all but the naive estimators had relatively good performance. However, the augmented inverse-probability-of-treatment weighting estimator showed the largest relative bias. Under dual model misspecification and near-positivity violations, all double-robust estimators were biased. Nevertheless, the targeted maximum likelihood estimator showed the best bias-variance trade-off, more precise estimates, and appropriate 95% confidence interval coverage, supporting the use of the data-adaptive model selection strategies based on machine learning algorithms. We applied these methods to estimate adjusted 1-year mortality risk differences in 183,426 lung cancer patients diagnosed after admittance to an emergency department versus persons with a nonemergency cancer diagnosis in England (2006-2013). The adjusted mortality risk (for patients diagnosed with lung cancer after admittance to an emergency department) was 16% higher in men and 18% higher in women, suggesting the importance of interventions targeting early detection of lung cancer signs and symptoms. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a wide-ranging collection of developmental diseases with varying symptoms and degrees of disability. Currently, ASD is diagnosed mainly with psychometric tools, often unable to provide an early and reliable diagnosis. Recently, biochemical methods are being explored as a means to meet the latter need. For example, an increased predisposition to ASD has been associated with abnormalities of metabolites in folate-dependent one carbon metabolism (FOCM) and transsulfuration (TS). Multiple metabolites in the FOCM/TS pathways have been measured, and statistical analysis tools employed to identify certain metabolites that are closely related to ASD. The prime difficulty in such biochemical studies comes from (i) inefficient determination of which metabolites are most important and (ii) understanding how these metabolites are collectively related to ASD. This paper presents a new method based on scores produced in Support Vector Machine (SVM) modeling combined with High Dimensional Model Representation (HDMR) sensitivity analysis. The new method effectively and efficiently identifies the key causative metabolites in FOCM/TS pathways, ranks their importance, and discovers their independent and correlative action patterns upon ASD. Such information is valuable not only for providing a foundation for a pathological interpretation but also for potentially providing an early, reliable diagnosis ideally leading to a subsequent comprehensive treatment of ASD. With only tens of SVM model runs, the new method can identify the combinations of the most important metabolites in the FOCM/TS pathways that lead to ASD. Previous efforts to find these metabolites required hundreds of thousands of model runs with the same data. [ABSTRACT FROM AUTHOR]

Background: Dento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision support system for treatment of dento-maxillofacial malformations. Methods: Patients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes. Results: The binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human–computer interaction. There was no statistically significant difference between the actual and AI- groups. Conclusions: Machine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers. [ABSTRACT FROM AUTHOR]

In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning techniques. In this paper we propose a Support Vector Machines (SVMs) based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease. The paper provides the implementation details along with the corresponding results for all the assessed classifiers. Several comparative studies have been carried out concerning both the prognosis and diagnosis problem demonstrating the superiority of the proposed SVM algorithm in terms of sensitivity, specificity and accuracy. [ABSTRACT FROM AUTHOR]

Machine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved, it would streamline the radiologists work. To deal with this complex three-dimensional problem, we propose a machine learning approach based on a 2D Faster RCNN network. We aimed to achieve a reliable system, i.e., with balanced sensitivity and precision. Therefore, we have researched and analysed, among others, impact of the way the training data are provided to the system, their pre-processing, the choice of model and its structure, and also the ways of regularisation. Furthermore, we also carefully analysed the network predictions and proposed an algorithm for its post-processing. The proposed approach enabled for obtaining high precision (89.74%), sensitivity (92.62%), and F1 score (90.84%). The paper presents the main challenges connected with automatic cerebral microbleeds detection, its deep analysis and developed system. The conducted research may significantly contribute to automatic medical diagnosis. [ABSTRACT FROM AUTHOR]

Early childhood anxiety disorders are common, impairing, and predictive of anxiety and mood disorders later in childhood. Epidemiological studies over the last decade find that the prevalence of impairing anxiety disorders in preschool children ranges from 0.3% to 6.5%. Yet, less than 15% of young children with an impairing anxiety disorder receive a mental health evaluation or treatment. One possible reason for the low rate of care for anxious preschoolers is the lack of affordable, timely, reliable and valid tools for identifying young children with clinically significant anxiety. Diagnostic interviews assessing psychopathology in young children require intensive training, take hours to administer and code, and are not available for use outside of research settings. The Preschool Age Psychiatric Assessment (PAPA) is a reliable and valid structured diagnostic parent-report interview for assessing psychopathology, including anxiety disorders, in 2 to 5 year old children. In this paper, we apply machine-learning tools to already collected PAPA data from two large community studies to identify sub-sets of PAPA items that could be developed into an efficient, reliable, and valid screening tool to assess a young child’s risk for an anxiety disorder. Using machine learning, we were able to decrease by an order of magnitude the number of items needed to identify a child who is at risk for an anxiety disorder with an accuracy of over 96% for both generalized anxiety disorder (GAD) and separation anxiety disorder (SAD). Additionally, rather than considering GAD or SAD as discrete/binary entities, we present a continuous risk score representing the child’s risk of meeting criteria for GAD or SAD. Identification of a short question-set that assesses risk for an anxiety disorder could be a first step toward development and validation of a relatively short screening tool feasible for use in pediatric clinics and daycare/preschool settings. [ABSTRACT FROM AUTHOR]

In recent years, intelligent structural damage diagnosis algorithms using machine learning have achieved much success. However, because of the fact that in real bridge applications, the working environment (load, temperature, and noise) is changing all the time, degradation of the performance of intelligent structural damage diagnosis methods is very serious. To address these problems, a novel bridge diagnosis algorithm based on deep learning is proposed. Our contributions include: First, we proposed an improved denoising auto‐encoder‐based deep neural networks, which is optimized by the gray relational analysis. It is able to automatically extract high‐level features from raw signals via a multi‐layer extraction to satisfy any damage diagnosis objective and thus does not need any time consuming denoising prepossessing. The model can achieve high accuracy under noisy environment. Second, the algorithm does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working environment is changed. Numerical simulations and experimental investigations on real bridges conducted to present the accuracy and efficiency of the proposed algorithm, comparing with other commonly machine learning‐based algorithms. The result shows it is deemed as an ideal and effective method for damage diagnosis of bridge structures. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Simple Summary: In this comprehensive review, we aimed to summarize the advances made by artificial intelligence in the field of lung cancer screening, diagnosis, and management. We now understand the utility of AI as a tool that can supplement physicians to improve the quality of care provided, which is the core message of this review, along with the relevant literature supporting the advances. Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients. [ABSTRACT FROM AUTHOR]

The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning. [ABSTRACT FROM AUTHOR]

Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead to misinformed conclusions. To illustrate this concern, the current paper critically evaluates and attempts to reproduce results from two studies (Wall et al. in Transl Psychiatry 2(4):e100, ; PloS One 7(8), ) that claim to drastically reduce time to diagnose autism using machine learning. Our failure to generate comparable findings to those reported by Wall and colleagues using larger and more balanced data underscores several conceptual and methodological problems associated with these studies. We conclude with proposed best-practices when using machine learning in autism research, and highlight some especially promising areas for collaborative work at the intersection of computational and behavioral science. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis. [ABSTRACT FROM AUTHOR]

BACKGROUND: Upper extremity function (UEF) is often compromised in multiple sclerosis (MS), although its importance is regularly underrecognized relative to ambulation. We explored the concurrent presence of impairment in UEF and ambulation by examining various aspects of UEF across different levels of ambulation. METHODS: The cohort consisted of 247 patients with clinically definite MS or clinically isolated syndrome according to the revised 2010 McDonald criteria. The Nine-Hole Peg Test and the Expanded Disability Status Scale were used to stratify patients into clinically different subgroups. For UEF, cerebellar function (finger-to-nose test), pyramidal function (pronator drift test), and the ability to perform a task of activities of daily living (drinking- from-cup test) were examined. Patient-reported limitations of UEF in daily life were assessed using the Arm Function in Multiple Sclerosis Questionnaire. RESULTS: Patients in more severely impaired ambulation groups displayed poorer performance on all UEF measures. Although most patients had normal to mild (n = 147) or moderate (n = 46) ambulatory impairment, 87.7% exhibited some level of UEF impairment as defined using the Nine-Hole Peg Test. Most patients had mild UEF impairment (n = 174), accounting for the largest proportion in all ambulation groups (51.9%-77.8%). CONCLUSIONS: A distinct pattern of impairment was found for ambulation and multiple aspects of UEF. Independent assessment of multiple aspects of disability may be helpful in treatment decision-making and could support the development of rehabilitation strategies that specifically target UEF impairment [ABSTRACT FROM AUTHOR]

Background: Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. Results: In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. Conclusion: The method of using RF outlier measure to design a nonconformity measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class. [ABSTRACT FROM AUTHOR]

In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods—Mutual Information, ReliefF, Chi Square, and Xvariance—and then each feature from the union set was assessed by three classification algorithms—support vector machine, naïve Bayes, and k-nearest neighbors—and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost. [ABSTRACT FROM AUTHOR]

Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children. [ABSTRACT FROM AUTHOR]

Exhaled breath analysis has become more and more popular as a supplementary tool for medical diagnosis. However, the number of variables that have to be taken into account forces researchers to develop novel algorithms for proper data interpretation. This paper presents a system for analyzing exhaled air with the use of various sensors. Breath simulations with acetone as a diabetes biomarker were performed using the proposed e-nose system. The XGBoost algorithm for diabetes detection based on artificial breath analysis is presented. The results have shown that the designed system based on the XGBoost algorithm is highly selective for acetone, even at low concentrations. Moreover, in comparison with other commonly used algorithms, it was shown that XGBoost exhibits the highest performance and recall. [ABSTRACT FROM AUTHOR]

A system's operational life cycle now includes an integrated health management and diagnostic strategy due to improvements in the current technology. It is evident that the life cycle may be used to identify abnormalities, analyze failures, and forecast future conditions based on current data. Data models can be trained using machine learning and statistical ideas, employing condition data and on-site feedback. Once data models are trained, the data-processing logic can be integrated into onboard controllers, allowing for real-time health evaluation and analysis. Interestingly, the oil and gas industries may encounter numerous obstacles and hurdles as a result of the integration, highlighting the need for creative solutions to the perplexing problem. The potential benefits in terms of challenges involving feature extraction and data classification, machine learning has received significant research attention recently. The application and utility in pump system health management should be investigated to explore the extend it can be used to increase overall system resilience or identify potential financial advantages for maintenance, repair, and overhaul activities. This is seen as an evolving research area, with a variety of application domains. This article present a critical analysis of machine learning's most current advances in the field of artificial intelligence-based system health management, specifically in terms of pump applications in the oil and gas industries. To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. There are, few drawbacks that is seen to be preventing its widespread adoption which prompt for further improvement. The article discussed possible solutions to the identified drawbacks and future opportunities presented. This study further elaborates on the commonly available commercial machine learning (ML) tools used for pump fault prognostics and diagnostics with an emphasis on the type of data utilized. Findings from the literature review shows that the neural network (NN) is the most prevalent algorithm employed in studies, followed by the Bayesian network (BN), support vector machine (SVM), and hybrid models. While the need for selecting appropriate training algorithms is seen to be significant. Interestingly, no specific method or algorithm exists for a given problem instead the solution relies on the type of data and the algorithm's or method's aptitude for resolving the provided errors. Among the various research studies on pump fault diagnosis and prognosis, the most frequently discussed problem is a bearing fault, with a percentage of 46%, followed by cavitation. The studies rank seal damage as the third most prevalent flaw. Leakage and obstruction are the least studied defects in research. The main data types used in machine learning techniques for diagnosing pump faults are vibration and flow, which might not be sufficient to identify the condition of pumps and their characteristics. The various datasets have been derived from expert opinion, real-world observations, laboratory tests, and computer simulations. Field data have frequently been used to create experimental datasets and simulated data. In comparison to the algorithmic approach, the data approach has not received significant research attention. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new Internet of Things (IoT) based predictive modelling by using fuzzy cluster based augmentation and classification for predicting the lung cancer disease through continuous monitoring and also to improve the healthcare by providing medical instructions. Here, the fuzzy clustering method is used and which is based on transition region extraction for effective image segmentation. Moreover, Fuzzy C-Means Clustering algorithm is used to categorize the transitional region features from the feature of lung cancer image. In this work, Otsu thresholding method is used for extracting the transition region from lung cancer image. Moreover, the right edge image and the morphological thinning operation are used for enhancing the performance of segmentation. In addition, the morphological cleaning and the image region filling operations are performed over an edge lung cancer image for getting the object regions. In addition, we also propose a new incremental classification algorithm which combines the existing Association Rule Mining (ARM), the standard Decision Tree (DT) with temporal features and the CNN. The experiments have been conducted by using the standard images that are collected from database and the current health data which are collected from patient through IoT devices. The results proved that the performance of the proposed prediction model which is able to achieve the better accuracy when it is compared with other existing prediction model. [ABSTRACT FROM AUTHOR]

Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a challenging task in medical science. Effective machine learning and soft computing methods should be used to facilitate Diabetic Retinopathy Diagnosis from Retinal Images. Artificial Neural Networks are widely preferred for Diabetic Retinopathy Diagnosis from Retinal Images. It was observed that the conventional neural networks especially the Hopfield Neural Network (HNN) may be inaccurate due to the weight values are not adjusted in the training process. This paper presents a new Modified Hopfield Neural Network (MHNN) for abnormality classification from human retinal images. It relies on the idea that both weight values and output values can be adjusted simultaneously. The novelty of the proposed method lies in the training algorithm. In conventional method, the weights remain fixed but the weights are changing in the proposed method. Experimental performed on the Lotus Eye Care Hospital containing 540 images collected showed that the proposed MHNN yields an average sensitivity and specificity of 0.99 and accuracy of 99.25%. The proposed MHNN is better than HNN and other neural network approaches for Diabetic Retinopathy Diagnosis from Retinal Images. [ABSTRACT FROM AUTHOR]

Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma's can occur in many parts of the brain and accordingly it is named. In this paper, a mixture model based classification of meningioma brain tumor using MRI image is developed. The proposed method consists of four stages. In the first stage, with respect to the cells' boundary, it is necessary to further processing, which ensures the boundary of some cells is a discrete region. Mathematical Morphology brings a fancy result during the discrete processing. Accurate cancer cell nucleus segmentation is necessary for automated cytological image analysis. Thresholding is a crucial step in segmentation..An adaptive binarization technique is an important step for medical image analysis.Finally, a novel hybrid Fuzzy SVM is designed in the classification stage meningioma brain tumor. The tumor classification results of proposed feature extraction with SVM is 74.24%, MM with FSVM is 82.67% and MM with RBF is 62.71% and our proposed method MM with Hybrid SVM is 91.64%. [ABSTRACT FROM AUTHOR]

We performed a systematic review to evaluate the success of machine learning algorithms in the diagnosis and prognostic prediction of dental caries. The review protocol was a priori registered in the PROSPERO, CRD42020183447. The search involved electronic bibliographic databases: PubMed/Medline, Scopus, EMBASE, Web of Science, and grey literature until December 2020. We excluded review articles, case series, case reports, editorials, letters, comments, educational methodologies, assessments of robotic devices, and articles with less than 10 participants or specimens. Two independent reviewers selected the studies and performed the assessment of the methodological quality based on standardized scales. We summarize data on the machine learning algorithms used; software; performance outcomes such as accuracy/precision, sensitivity/recall, specificity, area under the receiver operating characteristic curve (AUC), and positive/negative predictive values related to dental caries. Meta-analyses were not performed due to methodological differences. Our review included 15 studies (10 diagnostic studies and 5 prognostic prediction studies). Cross-sectional design studies were predominant (12). The most frequently used statistical measure of performance reported in diagnostic studies was AUC value, which ranged from 0.745 to 0.987. For most diagnostic studies, data from contingency tables were not available. Reported sensitivities were higher in low risk of bias prognostic prediction studies (median [IQR] of 0.996 [0.971-1.000] vs. unclear/high risk of bias studies 0.189 [0-0.340]; p value 0.025). While there were no significant differences in the specificity between these subgroups, we concluded that the use of these technologies for the diagnosis and prognostic prediction of dental caries, although promising, is at an early stage. The general applicability of the evidence was limited given that most models were developed outside the real clinical setting with a prevalence of unclear/high risk of bias. Researchers must increase the overall quality of their research protocols by providing a comprehensive report on the methods implemented. [ABSTRACT FROM AUTHOR]

Background: Arthritis is a common condition, which frequently involves the hands. Patients with inflammatory arthritis have been shown to experience significant delays in diagnosis. Aim: To develop and test a screening tool combining an image of a patient's hands, a short series of questions and a single examination technique to determine the most likely diagnosis in a patient presenting with hand arthritis. Machine learning techniques were used to develop separate algorithms for each component, which were combined to produce a diagnosis. Methods: A total of 280 consecutive new patients presenting to a rheumatology practice with hand arthritis were enrolled. Each patient completed a nine‐part questionnaire, had photographs taken of each hand and had a single examination result recorded. The rheumatologist diagnosis was recorded following a 45‐min consultation. The photograph algorithm was developed from 1000 previous hand images and machine learning techniques were applied to the questionnaire results, training several models against the diagnosis from the rheumatologist. Results: The combined algorithms in the present study were able to predict inflammatory arthritis with an accuracy, precision, recall and specificity of 96.8%, 97.2%, 98.6% and 90.5% respectively. Similar results were found when inflammatory arthritis was subclassified into rheumatoid arthritis and psoriatic arthritis. The corresponding figures for osteoarthritis were 79.6%, 85.9%, 61.9% and 92.6%. Conclusion: The present study demonstrates a novel application combining image processing and a patient questionnaire with applied machine‐learning methods to facilitate the diagnosis of patients presenting with hand arthritis. Preliminary results are encouraging for the application of such techniques in clinical practice. [ABSTRACT FROM AUTHOR]

The article describes the effect of Multiscale Principal Component Analysis (MSPCA) de-noising method in terms of epileptic seizure detection. Topics discussed include the development of a patient-independent seizure detection algorithm using Freiburg electroencephalogram (EEG) database and the application of MSPCA de-nosing method to EEG segment prior to Power Spectral Density (PSD). Also mentioned are the results showing that MSPCA is an effective de-noising method.

One of the most critical steps in medical health is the proper diagnosis of the disease. Dermatology is one of the most volatile and challenging fields in terms of diagnosis. Dermatologists often require further testing, review of the patient's history, and other data to ensure a proper diagnosis. Therefore, finding a method that can guarantee a proper trusted diagnosis quickly is essential. Several approaches have been developed over the years to facilitate the diagnosis based on machine learning. However, the developed systems lack certain properties, such as high accuracy. This study proposes a system developed in MATLAB that can identify skin lesions and classify them as normal or benign. The classification process is effectuated by implementing the K-nearest neighbor (KNN) approach to differentiate between normal skin and malignant skin lesions that imply pathology. KNN is used because it is time efficient and promises highly accurate results. The accuracy of the system reached 98% in classifying skin lesions. [ABSTRACT FROM AUTHOR]

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism. [ABSTRACT FROM AUTHOR]

Prompt and accurate diagnosis of infantile hemangiomas is essential to prevent potential complications. This can be difficult due to high rates of misdiagnosis and poor access to pediatric dermatologists. In this study, we trained an artificial intelligence algorithm to diagnose infantile hemangiomas based on clinical images. Our algorithm achieved a 91.7% overall accuracy in the diagnosis of facial infantile hemangiomas. [ABSTRACT FROM AUTHOR]