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The unavailability of appropriate mechanisms for timely detection of diseases and successive treatment causes the death of a large number of people around the globe. The timely diagnosis of grave diseases like different forms of cancer and other life-threatening diseases can save a valuable life or at least extend the life span of an afflicted individual. The advancement of the Internet of Medical Things (IoMT) enabled healthcare technologies can provide effective medical facilities to the population and contribute greatly towards the recuperation of patients. The usage of IoMT in the diagnosis and study of histopathological images can enable real-time identification of diseases and corresponding remedial actions can be taken to save an affected individual. This can be achieved by the use of imaging apparatus with the capacity of auto-analysis of captured images. However, most deep learning-based image classifying models are bulk in size and are inappropriate for use in IoT based imaging devices. The objective of this research work is to design a deep learning-based lightweight model suitable for histopathological image analysis with appreciable accuracy. This paper presents a novel lightweight deep learning-based model "ReducedFireNet", for auto-classification of histopathological images. The proposed method attained a mean accuracy of 96.88% and an F1 score of 0.968 on evaluating an actual histopathological image data set. The results are encouraging, considering the complexity of histopathological images. In addition to the high accuracy the lightweight design (size in few KBs) of the ReducedFireNet model, makes it suitable for IoMT imaging equipment. The simulation results show the proposed model has computational requirement of 0.201 GFLOPS and has a mere size of only 0.391 MB.

Background Following the inception of video games and the subsequent development of progressive innovations in videogame technology, many reports of videogame-related dermatologic conditions soon emerged in literature. Children and adolescents, in particular, are among the most avid consumers of online digital entertainment. Methods In an effort to better detail the impacts of these cutaneous manifestations and provide recommendations for injury prevention as it relates to video games, we performed an extensive systems-based literature review pertaining to the clinical features, etiology, diagnosis, and treatment of reported cases of dermatoses resulting from the misuse of video games. Results A literature review using PubMed, SCOPUS, Ovid MEDLINE and EMBASE was performed. Hardware implicated in the reported cases include video games consoles, personal computers, laptop computers, mobile phones and tablets. The dermatologic conditions are primarily localized to the extremities, particularly the palms and fingers. The majority are associated with repetitive friction and trauma, as well as allergic contact sensitivities. For all cutaneous manifestations induced by video gaming, early recognition and removal of the offending agent was most often described as effective in symptom resolution. Conclusions The universal use and pervasive popularity of video games for recreational purposes present an emerging dermatological concern. As videogames become increasingly advanced and immersive, various cutaneous conditions arising from intensive gaming will likely become common over time. Consequently, it is critical that dermatologists consider video games with a high index of suspicion when encountering dermatological ailments in underage patients engaging in persistent gaming behavior.

In dairy herd management, it is significant and irreplaceable for veterinarians to make rapid and effective diagnosis of dairy cow diseases. Based on electronic medical records, deep learning (DL) has been widely used to support clinical decisions for humans. However, this method is rarely adopted in veterinary diagnosis. In addition, most DL models are driven by large datasets, failing to utilize the knowledge acquired by veterinarians in subjective experience, which is critical to disease diagnosis. To address these problems, this paper proposes a DL method for disease diagnosis of dairy cow: convolutional neural network (CNN) based on knowledge graph and transfer learning (KGTL_CNN). Firstly, the structural knowledge was extracted from a knowledge graph of dairy cow diseases, and treated as part of the inputs to the CNN based on knowledge graph (KG_CNN). Then, the model performance was enhanced through pre-training by transfer learning. To verify its performance, experiments were carried out on dairy cow clinical datasets. The results show that our model performed satisfactorily on disease diagnosis: the KG_CNN and KGTL_CNN achieved an F1-score of 85.87% and 86.77%, respectively, higher than that of typical CNN by 6.58% and 7.7%. The research results greatly promote the effective, fast, and automatic clinical diagnosis of dairy cow diseases.

This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.

This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.

Background The rapid growth of mobile technology has given rise to the development of mobile health (mHealth) applications aimed at treating and preventing a wide range of health conditions. However, evidence on the use of mHealth in high disease burdened settings such as sub-Sharan Africa is not clear. Given this, we systematically mapped evidence on mHealth for disease diagnosis and treatment support by health workers in sub-Saharan Africa. Methods We conducted a scoping review study guided by the Arksey and O’Malley’s framework, Levac et al. recommendations, and Joanna Briggs Institute guidelines. We thoroughly searched the following databases: MEDLINE and CINAHL with full text via EBSCOhost; PubMed; Science Direct and Google Scholar for relevant articles from the inception of mHealth technology to April 2020. Two reviewers independently screened abstracts and full-text articles using the eligibility criteria as reference. This study employed the mixed methods appraisal tool version 2018 to assess the methodological quality of the included studies. Results Out of the 798 articles identified, only 12 published articles presented evidence on the availability and use of mHealth for disease diagnosis and treatment support by health workers in SSA since 2010. Of the 12 studies, four studies were conducted in Kenya; two in Malawi; two in Nigeria; one in South Africa; one in Zimbabwe; one in Mozambique, and one in Lesotho. Out of the 12 studies, one reported the use of mHealth for diseases diagnosis; three reported the use of mHealth to manage HIV; two on the management of HIV/TB; two on the treatment of malaria; one each on the management of hypertension; cervical cancer; and three were not specific on any disease condition. All the 12 included studies underwent methodological quality appraisal with a scored between 70 and 100%. Conclusions The study shows that there is limited research on the availability and use of mHealth by health workers for disease diagnosis and treatment support in sub-Saharan Africa. We, therefore, recommend primary studies focusing on the use of mHealth by health workers for disease diagnosis and treatment support in sub-Saharan Africa.

With the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826, Classification problems have an important role in the field of machine learning and data mining. Classification problems are used in different areas such as disease diagnosis, estimation of bank customers, drug studies, sentiment analysis. Many classification algorithms have been developed in the literature and these algorithms have many different parameter inputs. In this study, it is aimed to increase the classification success by using hyperparameter optimization algorithms. K-nearest neighbor, support vector machines, decision tree and gradient boosting classification algorithms were applied to the frequently used 'heart and iris' datasets in the literature. Grid search and random search algorithms, which are hyperparameter optimization algorithms, are applied to these selected classification algorithms. As a result of the experimental studies, it has been observed that the accuracy of all classification algorithms increases when hyperparameter optimization algorithms are applied. The parameter values that give the best results are shown. © 2021 IEEE

Brain network analysis has been proved to be one of the most effective methods in brain disease diagnosis. In order to construct discriminative brain networks and improve the performance of disease diagnosis, many machine learning–based methods have been proposed. Recent studies show that combining functional and structural brain networks is more effective than using only single modality data. However, in the most of existing multi-modal brain network analysis methods, it is a common strategy that constructs functional and structural network separately, which is difficult to embed complementary information of different modalities of brain network. To address this issue, we propose a unified brain network construction algorithm, which jointly learns both functional and structural data and effectively face the connectivity and node features for improving classification. First, we conduct space alignment and brain network construction under a unified framework, and then build the correlation model among all brain regions with functional data by low-rank representation so that the global brain region correlation can be captured. Simultaneously, the local manifold with structural data is embedded into this model to preserve the local structural information. Second, the PageRank algorithm is adaptively used to evaluate the significance of different brain regions, in which the interaction of multiple brain regions is considered. Finally, a multi-kernel strategy is utilized to solve the data heterogeneity problem and merge the connectivity as well as node information for classification. We apply the proposed method to the diagnosis of epilepsy, and the experimental results show that our method can achieve a promising performance.

To improve the recognition model accuracy of crop disease leaves and locating diseased leaves, this paper proposes an improved Faster RCNN to detect healthy tomato leaves and four diseases: powdery mildew, blight, leaf mold fungus and ToMV. First, we use a depth residual network to replace VGG16 for image feature extraction so we can obtain deeper disease features. Second, the k-means clustering algorithm is used to cluster the bounding boxes. We improve the anchoring according to the clustering results. The improved anchor frame tends toward the real bounding box of the dataset. Finally, we carry out a k-means experiment with three kinds of different feature extraction networks. The experimental results show that the improved method for crop leaf disease detection had 2.71% higher recognition accuracy and a faster detection speed than the original Faster RCNN.

Electronic health records are used to extract patient's information instantly and remotely, which can help to keep track of patients' due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using `HIPAA Safe Harbor' technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.

The scale of medical data is growing rapidly, and these data come from different data sources. The amount of data is huge, the production speed is fast, and the format is different. Case data is very important because it contains a lot of medical knowledge about diseases, drugs, treatments, etc. It can provide important support for the development of smart medicine. Knowledge graph is a graph-based data structure, which can well represent the relationship between these medical data in reality and form a semantic network. This research uses knowledge graph technology to connect trivial and scattered knowledge in various medical information systems to assist in disease diagnosis. This research takes thyroid disease as an example, constructs a medical knowledge graph and applies it to intelligent medical diagnosis. First, extract the relationships between biomedical entities to construct a biomedical knowledge graph. Then, the entities and relationships in the knowledge graph are transformed into low-dimensional continuous vectors through the knowledge graph embedding method. Finally, the known pathological disease relationship data is used to train the disease diagnosis model of the bidirectional long short-term memory network (BSTLM). Experiments show that the thyroid disease diagnosis method that combines knowledge graphs and deep learning has a better diagnostic effect. This shows that smart medical care based on the knowledge graph will provide a solution path for alleviating the shortage of domestic high-quality medical resources.

People are admitted to intensive care units (ICUs) because they need complete support for failing organ systems, constant monitoring, routine nursing care, and treatment. A critical or intensive illness is different from conventional or chronic diseases that most people are likely to have previously encountered. Such an illness is often unexpected and without warnings and can suddenly strike the previously fit. High levels of treatment and support are generally required to prevent life-threatening complications for the patents. Two of the most noticeable actions during an ICU stay are disease diagnosis and severity assessment of the patients. Unlike the majority of previous approaches where diagnosis and severity assessment are studied separately, we treat these actions as two tasks in an integrated procedure that clinicians must be able to quickly and accurately conduct such that patients are given the best possible chance for therapeutic success. In this paper, we propose an integrated disease diagnosis and severity assessment model (IDDSAM) to diagnose and assess diseases. Moreover, accompanying the prediction, we also provide an evidence-based explanation. IDDSAM is a multisource multitask model that is based on an attention mechanism and utilizes shareable information from laboratory tests, bedside monitoring, and complications to support patients' severity assessment and in-hospital disease diagnoses. We use 50,430 ICU cases consisting of 46,520 patients from 50 kinds of diseases over nine classifications to evaluate our proposed model. The experimental results demonstrated that our model outperforms the existing state-of-the-art mortality and diagnosis prediction framework by 3.79% on average in terms of accuracy for the mortality prediction tasks and by 14.51% on average for the diagnosis tasks.

Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

Background: The implementation of diagnosis coding in the Medical Record Unit at a health institution plays an important role in the administration of medical records at the hospital because it describes the quality management of medical records. In order to maintain the quality, it is crucial to accomplish the accreditation standard, especially at ICM. 13 related coding. Objective: This study aimed to understand the procedures of implementation, compliance disease diagnosis code execution in an outpatient based on accreditation standards KARS 2012, the percentage and the resistance of diagnosis coding implementation in outpatients. Methods: This research was a descriptive qualitative approach with cross sectional design. The subjects were medical records staff with Diploma 3 medical record education background, outpatients coding officer, reporting coordinator, the head of clinic space and a clinic nurse. The data collectin techniques used were observation, documentation and interview studies. Testing the validity of the data use triangulate of source and triangulate of techniq. Results: The coding was done by the medical records staffs and nurses, coding reference were in the form of policies, guidelines and standard operating procedure, guidelines used by nurses in coding was assistive book. Tugurejo Hospital Accreditation in Central Java province had fulfilled the five elements of ICM. 13 and passed the accreditation of type B-level plenary meeting. The percentage of outpatient coding implementation reached 78.6%, consisting of JKN amounted to 75.4% and 3.2% were non JKN. The barriers of coding implementation consists of five elements such as man, method, material, machine and money. Conclusion: In general the implementation of the coding in JKN outpatient has already done optimally, but for non JKN has not been optimal because of the inhibiting factors such as man, method, material, machine and money. Keywords: Coding, disease diagnosis, outpatient

This paper presents a systematic review of the literature and the classification of fuzzy logic application in an infectious disease. Although the emergence of infectious diseases and their subsequent spread have a significant impact on global health and economics, a comprehensive literature evaluation of this topic has yet to be carried out. Thus, the current study encompasses the first systematic, identifiable and comprehensive academic literature evaluation and classification of the fuzzy logic methods in infectious diseases. 40 papers on this topic, which have been published from 2005 to 2019 and related to the human infectious diseases were evaluated and analyzed. The findings of this evaluation clearly show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis. The key fuzzy logic methods used for the infectious disease are the fuzzy inference system; the rule-based fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy cognitive map. Furthermore, the accuracy, sensitivity, specificity and the Receiver Operating Characteristic (ROC) curve were universally applied for a performance evaluation of the fuzzy logic techniques. This thesis will also address the various needs between the different industries, practitioners and researchers to encourage more research regarding the more overlooked areas, and it will conclude with several suggestions for the future infectious disease researches.

13 Pág., This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water needs, weeds, and diseases. The general goal was proposing UAV-based technological solutions for a better use of agricultural and forestry resources and more efficient production with relevant economic and environmental benefits., This research was funded by the project AGL2017-83325-C4-1R of Agencia Española de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER). The contribution of Dr. Shi and Dr. Maja were supported by the Nebraska Agricultural Experiment Station through the Hatch Act capacity funding program (Accession Number 1011130) and Project No. SC-1700543 from the USDA National Institute of Food and Agriculture, respectively.

Mobile health (mHealth) technologies have been identified as promising strategies for improving access to healthcare delivery and patient outcomes. However, the extent of availability and use of mHealth among healthcare professionals in Ghana is not known. The study’s main objective was to examine the availability and use of mHealth for disease diagnosis and treatment support by healthcare professionals in the Ashanti Region of Ghana. A cross-sectional survey was carried out among 285 healthcare professionals across 100 primary healthcare clinics in the Ashanti Region with an adopted survey tool. We obtained data on the participants’ background, available health infrastructure, healthcare workforce competency, ownership of a mobile wireless device, usefulness of mHealth, ease of use of mHealth, user satisfaction, and behavioural intention to use mHealth. Descriptive statistics were conducted to characterise healthcare professionals’ demographics and clinical features. Multivariate logistic regression analysis was performed to explore the influence of the demographic factors on the availability and use of mHealth for disease diagnosis and treatment support. STATA version 15 was used to complete all the statistical analyses. Out of the 285 healthcare professionals, 64.91% indicated that mHealth is available to them, while 35.08% have no access to mHealth. Of the 185 healthcare professionals who have access to mHealth, 98.4% are currently using mHealth to support healthcare delivery. Logistic regression model analysis significantly (p &lt, 0.05) identified that factors such as the availability of mobile wireless devices, phone calls, text messages, and mobile apps are associated with HIV, TB, medication adherence, clinic appointments, and others. There is a significant association between the availability of mobile wireless devices, text messages, phone calls, mobile apps, and their use for disease diagnosis and treatment compliance from the chi-square test analysis. The findings demonstrate a low level of mHealth use for disease diagnosis and treatment support by healthcare professionals at rural clinics. We encourage policymakers to promote the implementation of mHealth in rural clinics.

This article introduces Arabica coffee leaf datasets known as JMuBEN and JMuBEN2. Image acquisition was done in Mutira coffee plantation in Kirinyaga county-Kenya under real-world conditions using a digital camera and with the help of a pathologist. JMuBEN dataset contains three compressed folders with images inside. The first file contains 7682 images of Cerscospora, the second contains 8337 images of rust and the last one contains 6572 images of Phoma. JMuBEN2 contains two compressed files where the first file contains 16,979 images of Miner while the other contains 18,985 images of healthy leaves. In total, the dataset contains 58,555 leaf images spread across five classes (Phoma, Cescospora, Rust, Healthy, Miner,) with annotations regarding the state of the leaves and the disease names. The Arabica datasets contain images that facilitates training and validation during the utilization of deep learning algorithms for coffee plant leaf disease recognition and classification. The dataset is publicly and freely available at https://data.mendeley.com/datasets/tgv3zb82nd/1 and https://data.mendeley.com/datasets/t2r6rszp5c/1 respectively.

3D printing is a smart additive manufacturing technique that allows the engineering of biomedical devices that are usually difficult to design using conventional methodologies such as machining or molding. Nowadays, 3D-printed microfluidics has gained enormous attention due to their various advantages including fast production, cost-effectiveness, and accurate designing of a range of products even geometrically complex devices. In this review, we focused on the recent significant findings in the field of 3D-printed microfluidic devices for biomedical applications. 3D printers are used as fabrication tools for a broad variety of systems for a range of applications like diagnostic microfluidic chips to detect different analytes, for example, glucose, lactate, and glutamate and the biomarkers related to different clinically relevant diseases, for example, malaria, prostate cancer, and breast cancer. 3D printers can print various materials (inorganic and polymers) with varying density, strength, and chemical properties that provide users with a broad variety of strategic options. In this article, we have discussed potential 3D printing techniques for the fabrication of microfluidic devices that are suitable for biomedical applications. Emerging diagnostic technologies using 3D printing as a method for integrating living cells or biomaterials into 3D printing are also reviewed.

The recent advancements in Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By incorporating key technologies such as IoT and AI, medical services can be improved. The convergence of IoT and AI offers different opportunities in healthcare sector. In this view, the current research article presents a new AI and IoT convergence-based disease diagnosis model for smart healthcare system. The major goal of this article is to design a disease diagnosis model for heart disease and diabetes using AI and IoT convergence techniques. The presented model encompasses different stages namely, data acquisition, preprocessing, classification, and parameter tuning. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. The proposed method uses Crow Search Optimization algorithm-based Cascaded Long Short Term Memory (CSO-CLSTM) model for disease diagnosis. In order to achieve better classification of the medical data, CSO is applied to tune both ‘weights’ and ‘bias’ parameters of CLSTM model. Besides, isolation Forest (iForest) technique is employed in this research work to remove the outliers. The application of CSO helps in considerable improvement in the diagnostic outcomes of CLSTM model. The performance of CSO-LSTM model was validated using healthcare data. During the experimentation, the presented CSO-LSTM model accomplished the maximum accuracies of 96.16% and 97.26% in diagnosing heart disease and diabetes respectively. Therefore, the proposed CSO-LSTM model can be employed as an appropriate disease diagnosis tool for smart healthcare systems.

To provide an available diagnostic model for diagnosing coronary atherosclerotic heart disease to provide an auxiliary function for doctors, we proposed a new evolutionary classification model in this paper. The core of the prediction model is a kernel extreme learning machine (KELM) optimized by an improved salp swarm algorithm (SSA). To get a better subset of parameters and features, the space transformation mechanism is introduced in the optimization core to improve SSA for obtaining an optimal KELM model. The KELM model for the diagnosis of coronary atherosclerotic heart disease (STSSA-KELM) is developed based on the optimal parameters and a subset of features. In the experiment, STSSA-KELM is compared with some widely adopted machine learning methods (MLM) in coronary atherosclerotic heart disease prediction. The experimental results show that STSSA-KELM can realize excellent classification performance and more robust stability under four indications. We also compare the convergence of STSSA-KELM with other MLM, the STSSA-KELM model has demonstrated a higher classification performance. Therefore, the STSSA-KELM model can effectively help doctors to diagnose coronary heart disease.

In the recent SARS-CoV-2 pandemic, public health experts have emphasized testing, tracking infected people, and tracing their contacts as an effective strategy to reduce the spread of the virus. Several diagnostic methods are reported for detecting the coronavirus in clinical, research, and public health laboratories. Some tests detect the infection directly by detecting the viral RNA and other tests detect the infection indirectly by detecting the host antibodies. A diagnostic test during the pandemic should help make an appropriate clinical decision in a short period of time. Recently reported diagnostic methods for SARS-CoV-2 have varying throughput, batching capacity, requirement of infrastructure setting, analytical performance, and turnaround times ranging from a few minutes to several hours. These factors should be considered while selecting a reliable and rapid diagnostic method to help make an appropriate decision and prompt public health interventions. This paper reviews recent SARS-CoV-2 diagnostic methods published in journals and reports released by regulatory agencies. We compared the analytical efficiency including limit of detection, sensitivity, specificity, and throughput. In addition, we also looked into ease of use, affordability, and availability of accessories. Finally, we discuss the limitations of the methods and provide our perspectives on priorities for future test development.

The advances in mobile technologies and applications are driving the transformation in health services delivery globally. Mobile phone penetration is increasing exponentially in low-and middle-income countries, hence using mobile phones for healthcare services could reach more people in resource-limited settings than the traditional forms of healthcare provision. The review presents recent literature on facilitators and barriers of implementing mHealth for disease screening and treatment support in low-and middle-income countries. We searched for relevant literature from the following electronic databases: MEDLINE; CINAHL with full text via EBSCOhost; Science Direct; PubMed; Google Scholar and Web of Science using the keywords for relevant studies. We searched for published studies from 2015 to August 2020 with no language limitations. A total of 721 articles identified, 125 articles met the inclusion criteria and were included in the qualitative synthesis. The review demonstrates relevant facilitators for the implementation of mHealth, which includes knowledge, attitudes, and perceptions of stakeholders on the use of mHealth and the performance of mHealth for disease diagnosis in low and-middle-income countries. Barriers and challenges hindering the implementation of mHealth applications were also identified. We proposed a framework for improving the implementation of mHealth for disease screening and treatment support in low-and middle-income countries., Mobile health applications; Disease screening; Disease diagnosis; Treatment support and low-and middle-income countries.

Background Artificial intelligence–based assistive diagnostic systems imitate the deductive reasoning process of a human physician in biomedical disease diagnosis and treatment decision making. While impressive progress in this area has been reported, most of the reported successes are applications of artificial intelligence in Western medicine. The application of artificial intelligence in traditional Chinese medicine has lagged mainly because traditional Chinese medicine practitioners need to perform syndrome differentiation as well as biomedical disease diagnosis before a treatment decision can be made. Syndrome, a concept unique to traditional Chinese medicine, is an abstraction of a variety of signs and symptoms. The fact that the relationship between diseases and syndromes is not one-to-one but rather many-to-many makes it very challenging for a machine to perform syndrome predictions. So far, only a handful of artificial intelligence–based assistive traditional Chinese medicine diagnostic models have been reported, and they are limited in application to a single disease-type. Objective The objective was to develop an artificial intelligence–based assistive diagnostic system capable of diagnosing multiple types of diseases that are common in traditional Chinese medicine, given a patient’s electronic health record notes. The system was designed to simultaneously diagnose the disease and produce a list of corresponding syndromes. Methods Unstructured freestyle electronic health record notes were processed by natural language processing techniques to extract clinical information such as signs and symptoms which were represented by named entities. Natural language processing used a recurrent neural network model called bidirectional long short-term memory network–conditional random forest. A convolutional neural network was then used to predict the disease-type out of 187 diseases in traditional Chinese medicine. A novel traditional Chinese medicine syndrome prediction method—an integrated learning model—was used to produce a corresponding list of probable syndromes. By following a majority-rule voting method, the integrated learning model for syndrome prediction can take advantage of four existing prediction methods (back propagation, random forest, extreme gradient boosting, and support vector classifier) while avoiding their respective weaknesses which resulted in a consistently high prediction accuracy. Results A data set consisting of 22,984 electronic health records from Guanganmen Hospital of the China Academy of Chinese Medical Sciences that were collected between January 1, 2017 and September 7, 2018 was used. The data set contained a total of 187 diseases that are commonly diagnosed in traditional Chinese medicine. The diagnostic system was designed to be able to detect any one of the 187 disease-types. The data set was partitioned into a training set, a validation set, and a testing set in a ratio of 8:1:1. Test results suggested that the proposed system had a good diagnostic accuracy and a strong capability for generalization. The disease-type prediction accuracies of the top one, top three, and top five were 80.5%, 91.6%, and 94.2%, respectively. Conclusions The main contributions of the artificial intelligence–based traditional Chinese medicine assistive diagnostic system proposed in this paper are that 187 commonly known traditional Chinese medicine diseases can be diagnosed and a novel prediction method called an integrated learning model is demonstrated. This new prediction method outperformed all four existing methods in our preliminary experimental results. With further improvement of the algorithms and the availability of additional electronic health record data, it is expected that a wider range of traditional Chinese medicine disease-types could be diagnosed and that better diagnostic accuracies could be achieved.

Diabetic retinopathy (DR) is an important disease leading to blindness in humans, attracting a lot of research interests. Previous breakthrough research findings rely on deep learning techniques to diagnose diabetic retinopathy in patients with medical imaging. Although the medical imaging achieves reasonable recognition accuracy, the application of mass, easy-to-obtain and free electronic health records (EHR) data in life can make an early diagnosis of the DR more convenient and quick. In this paper, we used a set of five machine learning models to diagnose the DR in patients with the EHR data and formed a set of treatment methods. Our experimental data set is formed by processing the data provided by 301 hospitals. The experimental results show that random forest (RF) in the machine learning model can get 92% accuracy with good performance. Subsequently, the input features were analyzed and their importance graded to find that the predisposing factors triggering the human DR disease were associated with renal and liver function. In addition, disease diagnosis methods based on readily available the EHR data will become an integral part of smart healthcare and mobile healthcare.

Virtual assistants are involved in the daily activities of humans such as managing calendars, making appointments, and providing wake-up calls. They provide a conversational service to customers around-the-clock and make their daily life manageable. With this emerging trend, many well-known companies launched their own virtual assistants that manage the daily routine activities of customers. In the healthcare sector, virtual medical assistants also provide a list of relevant diseases linked to a specific symptom. Due to low accuracy and uncertainty, these generated recommendations are untrusted and may lead to hypochondriasis. In this study, we proposed a Medical Instructed Real-time Assistant (MIRA) that listens to the user&rsquo, s chief complaint and predicts a specific disease. Instead of informing about the medical condition, the user is referred to a nearby appropriate medical specialist. We designed an architecture for MIRA that considers the limitations of existing virtual medical assistants such as weak authentication, lack of understanding multiple intent statements about a specific medical condition, and uncertain diagnosis recommendations. To implement the designed architecture, we collected the chief complaints along with the dialogue corpora of real patients. Then, we manually validated these data under the supervision of medical specialists. We then used these data for natural language understanding, disease identification, and appropriate response generation. For the prototype version of MIRA, we considered the cases of glaucoma (eye disease) and diabetes (an autoimmune disease) only. The performance measure of MIRA was evaluated in terms of accuracy (89%), precision (90%), sensitivity (89.8%), specificity (94.9%), and F-measure (89.8%). The task completion was calculated using Cohen&rsquo, s Kappa ( k = 0 . 848 ) that categorizes MIRA as &lsquo, Almost Perfect&rsquo, Furthermore, the voice-based authentication identifies the user effectively and prevent against masquerading attack. Simultaneously, the user experience shows relatively good results in all aspects based on the User Experience Questionnaire (UEQ) benchmark data. The experimental results show that MIRA efficiently predicts a disease based on chief complaints and supports the user in decision making.

Rongbing Jin,1,* Jingjie Ge,2,* Ping Wu,2 Jiaying Lu,2 Huiwei Zhang,2 Jian Wang,3 Jianjun Wu,3 Xianhua Han,2 Weishan Zhang,2 Chuantao Zuo2,4 1Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing 400042, China; 2PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China; 3Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China; 4Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200433, China *These authors contributed equally to this work Purpose: We previously identified disease-related cerebral metabolic characteristics associated with Parkinson’s disease (PD) in the Chinese population using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) imaging. The present study aims to assess data reproducibility and robustness of the metabolic activity characteristics across independent cohorts. Patients and methods: Forty-eight patients with PD and 48 healthy controls from Chongqing district, in addition to 33 patients with PD and 33 healthy controls from Shanghai district were recruited. Each subject underwent brain 18F-FDG PET/CT imaging in a resting state. Based on the brain images, differences between the groups and PD-related cerebral metabolic activities were graphically and quantitatively evaluated. Results: Both PD patient cohorts exhibited analogous cerebral patterns characterized by metabolic increase in the putamen, globus pallidus, thalamus, pons, sensorimotor cortex and cerebellum, along with metabolic decrease in parieto-occipital areas. Additionally, the metabolic pattern was highly indicative of the disease, with a significant elevation in PD patients compared with healthy controls (p

Diagnosis of deadly diseases, such as liver fibrosis, is very important. Clinical decision support systems (CDSSs) based on patient’s historical medical data and accurate AI techniques can aid physicians in their decision-making process. The task of arriving at an accurate and timely diagnosis decision is always complex because of the dynamic, vagueness, and uncertainty associated with this disease. Fuzzy logic can perfectly handle these issues. In recent years, two of the most interesting techniques are a fuzzy analytical hierarchy process (FAHP) and an adaptive neuro-fuzzy inference system (ANFIS). The FAHP is popular for dealing with uncertainty in multi-criteria decision-making, and the ANFIS is popular in learning fuzzy inference system from data based on artificial neural networks. To the best of our knowledge, these two methods have not been used to model CDSSs in fibrosis stage detection domain. In this paper, we develop a CDSS based on a case comparison of the effectiveness of the FAHP and the ANFIS in the medical diagnosis of the fibrosis disease. We carefully design and implement two frameworks based on these two techniques. Diagnostic real data of 119 cases infected by chronic viral hepatitis C from the Liver Institute at Mansoura University in Egypt are used to train and test both the FAHP and ANFIS. Criteria and subcriteria weights are based on opinions of two domain experts. The ANFIS model is designed using trial and error based on the analysis of various experiments. Results are later compared with the diagnostic conclusions of medical expert and other three medical and fuzzy techniques. The comparison results show that these two techniques can successfully be employed in designing a diagnostic CDSS system for fibrosis diagnosis. The two techniques achieve a classification accuracy of 93.3%. The results confirm the efficiency and effectiveness of both methods. Therefore, both the FAHP and ANFIS are viable approaches in modeling CDSS for diagnosis of a liver fibrosis stage.

This paper proposes a method that extracts a feature set for accurate disease diagnosis from a feature (aptamer) array. Our method uses an artificial intelligence of the neural network and 10-fold cross-validations and is verified by the p-value of the aptamer array response to specimens of 80 liver cancer patients and 310 healthy people. The proposed method is compared with the one-way ANOVA method in terms of accuracy, the number of features, and computing time to determine the feature set required to achieve the same accuracy. An increase in the number of features dramatically improves the diagnosis accuracy of the two methods for 2–10 features. The accuracies with 10 features are 93.5% and 87.5%, and the increases in accuracy per additional feature are 3.39% and 2.65% for our method and the one-way ANOVA, respectively. For the same accuracy, our method needs only 1/2–1/3 number of features of the ANOVA. An interesting statistical characteristic of cross-validation is that diagnostic accuracy saturates after 10 000 cross-validations.

The novel Coronavirus named COVID-19 that World Health Organization (WHO) announced as a pandemic rapidly spread worldwide. Fast diagnosis of the virus infection is critical to prevent further spread of the virus, help identify the infected population, and cure the patients. Due to the increasing rate of infection and the limitations of the diagnosis kit, auxiliary detection tools are needed. Recent studies show that a deep learning model that comes up with the salient information of CT images can aid in the COVID-19 diagnosis. This study proposes a novel deep learning structure that the pooling layer of this model is a combination of pooling and the Squeeze Excitation Block (SE-block) layer. The proposed model uses Batch Normalization and Mish Function to optimize convergence time and performance of COVID-19 diagnosis. A dataset of two public hospitals was used to evaluate the proposed model. Moreover, it was compared to some different popular deep neural networks (DNN). The results expressed an accuracy of 99.03 with a recognition time of test mode of 0.069 ms in graphics processing unit (GPU). Furthermore, the best network results in classification metrics parameters and real-time applications belong to the proposed model.

Von Willebrand disease (VWD) is one of the main inherited coagulation disorders. It is caused by a deficiency and/or a dysfunction of the von Willebrand factor (VWF), a fundamental multimeric glycoprotein involved in the hemostasis process. Correct detection of the disease is not an easy task because the disease manifests itself in many variants and a high intra-subject variability is observed. For these reasons, the diagnostic clinical trials typically rely on a 24-h sampling protocol, which makes the overall test long, stressful, and costly. Using a new pharmacokinetic model derived from Galvanin et al.'s 2014 study, this study aims at i) assessing the theoretical possibility to perform a shorter clinical test and ii) proposing a set of model-based diagnostic methods as a support for the clinical team. A preliminary information analysis is performed in order to understand which sampling instants are more informative for model identification. This allowed us to propose a novel, 8-h diagnostic protocol, which is still able to ensure model identifiability. Three alternative diagnostic methods are then proposed based on this short-length clinical protocol. One of them directly uses the pharmacokinetic model, whereas the other two are based on the use of three indices (two pharmacokinetic indices, namely clearance, total VWF released, and as third index the basal multimer ratio) to formulate the diagnosis problem as a classification one. The classification problem is then solved using K-nearest neighbours and linear discriminant analysis. Results show the theoretical feasibility of a VWD diagnosis based on a shorter protocol.

Alzheimer's disease (AD) is a complex neurodegenerative disease involving a variety of pathogenic factors, and the etiology detection of this disease has been a major concern of researchers. Neuroimaging is a basic and important means to explore the problem. It is the main current scientific research direction for combining neuroimaging with other modal data to dig deep into the potential information of AD through the complementarities among multiple data points. Machine learning methods possess great potentiality and have reached some achievements in this research area. A few studies have proposed some solutions to the effects of multimodal data fusion, however, the overall analytical framework for data fusion and fusion result analysis has thus far been ignored. In this paper, we first put forward a novel multimodal data fusion method, and further present a new machine learning framework of data fusion, classification, feature selection, and disease-causing factor extraction. The real dataset of 37 AD patients and 35 normal controls (NC) with functional magnetic resonance imaging (fMRI) and genetic data was used to verify the effectiveness of the framework, which was more accurate in classification and optimal feature extraction than other methods. Furthermore, we revealed disease-causing brain regions and genes, such as the olfactory cortex, insula, posterior cingulate gyrus, lingual gyrus, CNTNAP2, LRP1B, FRMD4A, and DAB1. The results show that the machine learning framework could effectively perform multimodal data fusion analysis, providing new insights and perspectives for the diagnosis of Alzheimer's disease.

Background Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

Medical images are valuable sources for disease diagnosis. Besides, advancements in deep learning in recent years have been supporting disease diagnosis methods based on images to obtain numerous achievements. However, deep learning algorithms still work as a black-box so it is difficult to interpret output from these algorithms. In this study, we propose a convolutional network architecture to classify Chest X-ray images as well as apply explanation approaches for trained models to support disease diagnosis. The proposed method provides insights in medical images to support Pneumonia diagnosis.

As is known, cerebral stroke has become one of the main diseases endangering people&rsquo, s health, ischaemic strokes accounts for approximately 85% of cerebral strokes. According to research, early prediction and prevention can effectively reduce the incidence rate of the disease. However, it is difficult to predict the ischaemic stroke because the data related to the disease are multi-modal. To achieve high accuracy of prediction and combine the stroke risk predictors obtained by previous researchers, a method for predicting the probability of stroke occurrence based on a multi-model fusion convolutional neural network structure is proposed. In such a way, the accuracy of ischaemic stroke prediction is improved by processing multi-modal data through multiple end-to-end neural networks. In this method, the feature extraction of structured data (age, gender, history of hypertension, etc.) and streaming data (heart rate, blood pressure, etc.) based on a convolutional neural network is first realized. A neural network model for feature fusion is then constructed to realize the feature fusion of structured data and streaming data. Finally, a predictive model for predicting the probability of stroke is obtained by training. As shown in the experimental results, the accuracy of ischaemic stroke prediction reached 98.53%. Such a high prediction accuracy will be helpful for preventing the occurrence of stroke.

In this chapter, a novel classification methodology for medical disease diagnosis is proposed. The proposed classification operator comprises a stacked autoencoder network cascaded with a softmax layer. The classifier is trained by applying a special training approach, where each layer of the proposed classifier is trained individually and sequentially. The performance of the proposed classifier is compared with a number of representative classification methods from the literature. The experimental results on medical data sets show that the proposed classifier performs better than or at least competitive with classifiers used in this chapter. It is also seen that the proposed classifier can efficiently be used for the diagnosis of medical diseases provided that it is trained with a suitable data set with a sufficient number of medical features obtained from a sufficient number of patients. © 2019, Springer Nature Switzerland AG.

Poornima Singh,1 Sanjay Singh,2 Gayatri S Pandi-Jain1 1L. J. Institute of Engineering and Technology, Gujarat Technological University, 2Institute of Life Sciences, School of Science and Technology, Ahmedabad University, Ahmedabad, Gujarat, India Abstract: The health care industries collect huge amounts of data that contain some hidden information, which is useful for making effective decisions. For providing appropriate results and making effective decisions on data, some advanced data mining techniques are used. In this study, an effective heart disease prediction system (EHDPS) is developed using neural network for predicting the risk level of heart disease. The system uses 15 medical parameters such as age, sex, blood pressure, cholesterol, and obesity for prediction. The EHDPS predicts the likelihood of patients getting heart disease. It enables significant knowledge, eg, relationships between medical factors related to heart disease and patterns, to be established. We have employed the multilayer perceptron neural network with backpropagation as the training algorithm. The obtained results have illustrated that the designed diagnostic system can effectively predict therisk level of heart diseases. Keywords: data mining, neural network, multilayer perceptron neural network, backpropagation, disease diagnosis

Functional connectivity (FC) network has been becoming an increasingly useful tool for understanding the cerebral working mechanism and mining sensitive biomarkers for neural/mental disease diagnosis. Currently, Pearson's Correlation (PC) is the simplest and most commonly used scheme in FC estimation. Despite its empirical effectiveness, PC only encodes the low-order (i.e., second-order) statistics by calculating the pairwise correlations between network nodes (brain regions), which fails to capture the high-order information involved in FC (e.g., the correlations among different edges in a network). To address this issue, we propose a novel FC estimation method based on Matrix Variate Normal Distribution (MVND), which can capture both low- and high-order correlations simultaneously with a clear mathematical interpretability. Specifically, we first generate a set of BOLD subseries by the sliding window scheme, and for each subseries we construct a temporal FC network by PC. Then, we employ the constructed FC networks as samples to estimate the final low- and high-order FC networks by maximizing the likelihood of MVND. To illustrate the effectiveness of the proposed method, we conduct experiments to identify subjects with Mild Cognitive Impairment (MCI) from Normal Controls (NCs). Experimental results show that the fusion of low- and high-order FCs can generally help to improve the final classification performance, even though the high-order FC may contain less discriminative information than its low-order counterpart. Importantly, the proposed method for simultaneous estimation of low- and high-order FCs can achieve better classification performance than the two baseline methods, i.e., the original PC method and a recent high-order FC estimation method.

The interpretation of estimates of model parameters in terms of biological information is often just as important as the predictions of the model itself. In this study we consider the identification of metabolites in a possibly biologically heterogeneous case group that show abnormal patterns with respect to a set of (healthy) control observations. For this purpose, we filter normal (baseline) natural variation from the data by projection of the data on a control sample model: the residual approach. This step should more easily highlight the abnormal metabolites. Interpretation is, however, hindered by a problem we named the ‘residual bias’ effect, which may lead to the identification of the wrong metabolites as ‘abnormal’. This effect is related to the smearing effect. We propose to alleviate residual bias by considering a weighted average of the filtered and raw data. This way, a compromise is found between excluding irrelevant natural variation from the data and the amount of residual bias that occurs. We show for simulated and real-world examples that this compromise may outperform inspection of the raw or filtered data. The method holds promise in numerous applications such as disease diagnoses, personalized healthcare, and industrial process control.

By incorporating prior information on disease prevalence, Bayes classifiers have the potential to estimate disease probability better than other common machine-learning methods. Thus, it is important to develop Bayes classifiers specifically tailored for microbiome data. Our method shows higher classification accuracy than the only existing Bayesian classifier and the popular random forest method, and thus provides an alternative option for using microbial compositions for disease diagnosis., Dysbiosis of microbial communities is associated with various human diseases, raising the possibility of using microbial compositions as biomarkers for disease diagnosis. We have developed a Bayes classifier by modeling microbial compositions with Dirichlet-multinomial distributions, which are widely used to model multicategorical count data with extra variation. The parameters of the Dirichlet-multinomial distributions are estimated from training microbiome data sets based on maximum likelihood. The posterior probability of a microbiome sample belonging to a disease or healthy category is calculated based on Bayes’ theorem, using the likelihood values computed from the estimated Dirichlet-multinomial distribution, as well as a prior probability estimated from the training microbiome data set or previously published information on disease prevalence. When tested on real-world microbiome data sets, our method, called DMBC (for Dirichlet-multinomial Bayes classifier), shows better classification accuracy than the only existing Bayesian microbiome classifier based on a Dirichlet-multinomial mixture model and the popular random forest method. The advantage of DMBC is its built-in automatic feature selection, capable of identifying a subset of microbial taxa with the best classification accuracy between different classes of samples based on cross-validation. This unique ability enables DMBC to maintain and even improve its accuracy at modeling species-level taxa. The R package for DMBC is freely available at https://github.com/qunfengdong/DMBC. IMPORTANCE By incorporating prior information on disease prevalence, Bayes classifiers have the potential to estimate disease probability better than other common machine-learning methods. Thus, it is important to develop Bayes classifiers specifically tailored for microbiome data. Our method shows higher classification accuracy than the only existing Bayesian classifier and the popular random forest method, and thus provides an alternative option for using microbial compositions for disease diagnosis.

A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of seventeen (17) features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e. global best particles) that represent a pruned down collection of seven (7) features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: The All-features (i.e. classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e. QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach marginally improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

Background: BIOMARKAPD seeks to diminish the barriers associated with the clinical use of cerebrospinal fluid (CSF) biomarker analysis by reducing variation in CSF laboratory methodologies and generating consensus recommendations on their clinical interpretation and application for dementia diagnosis.Objective: To examine the disparity in practitioner attitudes and clinical practice relating to the use of CSF biomarkers for dementia diagnosis across Europe.Methods: Clinical dementia experts were surveyed on the prevalence of national consensus guidelines and analytical reimbursement across Europe, their biomarker platform preferences, lumbar puncture methodologies and application of reference values and cut-offs for CSF analysis.Results: 74% of respondents (total n = 51) use CSF biomarkers in clinical practice and 69% perform lumbar punctures on an outpatient basis. Most use CSF biomarkers to diagnose atypical (84%) and early-onset cases of cognitive impairment (71%) and for the differential diagnosis of other dementias (69%). 82% state they are sufficiently informed about CSF biomarkers yet 61% report a lack of national consensus guidelines on their use for dementia diagnosis. 48% of countries represented do not reimburse clinical CSF analysis costs. 43% report using normal reference ranges derived from publications.Conclusion: Variations in attitude and practice relating to CSF biomarkers, widely recognised as barriers to their clinical acceptance, remain evident within and between countries across Europe, even in expert centres. These shortcomings must be addressed by developing consensus guidelines on CSF-related methodologies and their clinical application, to further their use for the diagnostic evaluation of dementia.

In the last few years, different machine learning techniques such as supervised, unsupervised, and reinforcement learning have been effectively employed to solve distinct real-life multidisciplinary problems. These techniques have been effectively applied to accurately predict the problems related to stock values, disease diagnosis, sentiment analysis, text processing, gene classification, crop prediction, and weather forecasting. The objective of this manuscript is to present the systematic review on the use of these techniques in five major domains i.e. agriculture, finance, healthcare, education and engineering. A standard review methodology has been adapted to include and exclude the related literature. The performance of different supervised and nature-inspired computing techniques have been accessed on the basis of different performance metrics. The publication trend on the use of machine learning techniques in these five research areas has been also explored. Finally, the gaps in the study have been identified that will assist prospective researchers who want to pursue their research in these areas.

To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

OBJECTIVE: The aim of this study was to evaluate the diagnostic value of x-ray dark-field imaging in projection radiography-based depiction of pneumothoraces in the neonatal murine lung, a potentially life-threatening medical condition that requires a timely and correct diagnosis. MATERIALS AND METHODS: By the use of a unique preclinical model, 7-day-old C57Bl/6N mice received mechanical ventilation for 2 or 8 hours with oxygen-rich gas (FIO2 = 0.4; n = 24). Unventilated mice either spontaneously breathed oxygen-rich gas (FIO2 = 0.4) for 2 or 8 hours or room air (n = 22). At the end of the experiment, lungs were inflated with a standardized volume of air after a lethal dose of pentobarbital was administered to the pups. All lungs were imaged with a prototype grating-based small-animal scanner to acquire x-ray transmission and dark-field radiographs. Image contrast between the air-filled pleural space and lung tissue was quantified for both transmission and dark-field radiograms. After the independent expert's assessment, 2 blinded readers evaluated all dark-field and transmission images for the presence or absence of pneumothoraces. Contrast ratios, diagnostic accuracy, as well as reader's confidence and interreader agreement were recorded for both imaging modalities. RESULTS: Evaluation of both x-ray transmission and dark-field radiographs by independent experts revealed the development of a total of 10 pneumothoraces in 8 mice. Here, the contrast ratio between the air-filled pleural space of the pneumothoraces and the lung tissue was significantly higher in the dark field (8.4 ± 3.5) when compared with the transmission images (5.1 ± 2.8; P < 0.05). Accordingly, the readers' diagnostic confidence for the diagnosis of pneumothoraces was significantly higher for dark-field compared with transmission images (P = 0.001). Interreader agreement improved from moderate for the analysis of transmission images alone (κ = 0.41) to very good when analyzing dark-field images alone (κ = 0.90) or in combination with transmission images (κ = 0.88). Diagnostic accuracy significantly improved for the analysis of dark-field images alone (P = 0.04) or in combination with transmission images (P = 0.02), compared with the analysis of transmission radiographs only. CONCLUSIONS: The significant improvement in contrast ratios between lung parenchyma and free air in the dark-field images allows the facilitated detection of pneumothoraces in the newborn mouse. These preclinical experiments indicate the potential of the technique for future clinical applications.

In this study, artificial neural networks (ANN) method is used for the diagnosis of rheumatoid arthritis in order to support medical diagnostics. For the diagnosis of rheumatoid arthritis, backpropagation algorithm was examined in Matlab R2015b environment in artificial neural networks. With the system, the data in a data set, which are received from the patients with rheumatoid arthritis and from the people who are not suffering from rheumatoid arthritis, are classified successfully. Also, ANN backpropagation algorithm results and the results found by Perceptron algorithm are compared in terms of performance. Whereas %82 accuracy percentage is obtained with the Backpropagation method in performance tests in the data set, the accuracy percentage is calculated %71 with Perceptron method.

Nuclear and nuclear-related technologies have played an important role in animal health, particularly in relation to disease diagnosis and characterization of pathogenic organisms. This review focuses primarily on how and where nuclear technologies, both non-isotopic and isotopic methods, have made their impact in the past and where it might be expected they could have an impact in the future. The review outlines the extensive use of radiation attenuation in attempts to create vaccines for a multiplicity of pathogenic organisms and how the technology is being re-examined in the light of recent advances in irradiation techniques and cryopreservation/lyophilization that might obviate some of the problems of maintenance of viable, attenuate vaccines and their transport and use in the field. This approach could be used for a number of parasitic diseases where vaccination has been problematic and where investigations into the development of molecular vaccines have still failed to deliver satisfactory candidates for generating protective immune responses. Irradiation of antigens or serum samples also has its uses in diagnosis, especially when the samples need to be transported across international boundaries, or when handling the pathogens in question when carrying out a test presents serious health hazards to laboratory personnel. The present-day extensive use of enzyme immunoassays and molecular methods (e.g., polymerase chain reaction) for diagnosis and characterization of animal pathogens has its origins in the use of isotope-labeled antigens and antibodies. These isotopic techniques that included the use of 75Se, 32P, 125I, and 35S isotopes enabled a level of sensitivity and specificity that was hitherto unrealized, and it is prescient to remind ourselves of just how successful these technologies were, in spite of their infrequent use nowadays. Finally, the review looks at the potential for stable isotope analysis for a variety of applications--in the tracking of animal migrations, where the migrant are potential carriers of transboundary animal diseases, and where it would be useful to determine the origins of the carrier, e.g., Highly Pathogenic Avian Influenza and its dissemination by wild water fowl. Other applications could be in monitoring sequestered microbial culture (e.g., rinderpest virus) where in the case of accidental or deliberate release of infective culture it would be possible to identify the laboratory from which the isolate originated.

Image processing has been extensively used in various (human, animal, plant) disease diagnosis approaches, assisting experts to select the right treatment. It has been applied to both images captured from cameras of visible light and from equipment that captures information in invisible wavelengths (magnetic/ultrasonic sensors, microscopes, etc.). In most of the referenced diagnosis applications, the image is enhanced by various filtering methods and segmentation follows isolating the regions of interest. Classification of the input image is performed at the final stage. The disease diagnosis approaches based on these steps and the common methods are described. The features extracted from a plant/skin disease diagnosis framework developed by the author are used here to demonstrate various techniques adopted in the literature. The various metrics along with the available experimental conditions and results presented in the referenced approaches are also discussed. The accuracy achieved in the diagnosis methods that are based on image processing is often higher than 90%. The motivation for this review is to highlight the most common and efficient methods that have been employed in various disease diagnosis approaches and suggest how they can be used in similar or different applications.