AI Technologies for eHealth and mHealth

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 3385

Special Issue Editors


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Guest Editor
Medical Informatics Research & Development Center, University of Pannonia, 8200 Veszprém, Hungary
Interests: health informatics; data modeling; data analysis; expert systems

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Guest Editor
Department of Preventive Medicine, University of Szeged, 6700 Szeged, Hungary
Interests: medical informatics; m-health; telemedicine

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Guest Editor
Medical Informatics Research & Development Center, University of Pannonia, 8200 Veszprém, Hungary
Interests: medical knowledge management; coding systems

Special Issue Information

Dear Colleagues,

The ongoing ageing of modern societies will lead to a growing portion of society relying on the financial support of a shrinking workforce. At the same time, the sustainability of public health services is expected to be further affected by the application of new sophisticated and expensive imaging and diagnostic tools and processes developed for the care of widespread chronic diseases such as diabetes, cancer, and cardiovascular and neurological diseases. These trends will inevitably necessitate changes in the current health care system even in the short run, and the only realistic answer to this challenge is preventive self-management supported by ambient assisted living devices and IoT, and modern information technology relying on artificial intelligence. It is the objective of this Special Issue to give a cross-section of current research related to all applications of AI in the eHealth and mHealth domain, with an emphasis on the following fields:

  • Machine learning methods used for feature extraction, diagnostics and personalized treatment recommendations. Within this field, a special highlight is personalized and mobile lifestyle counselling for chronic disease prevention and management.
  • Medical expert systems using traditional rule-based or case-based reasoning, or evolutionary/swarm-intelligence algorithms, assisting medical professionals in research or daily care.
  • Natural language processing methods for analyzing current and legacy textual records and implementing intelligent chat support for patients.
  • This Special Issue welcomes original research articles and review papers. Case studies and reports on controlled clinical trials are especially welcome.

Dr. István Vassányi
Dr. István Kósa
Dr. László Balkányi
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • eHealth
  • ambient assisted living
  • personalized care
  • machine learning
  • medical expert systems

Published Papers (3 papers)

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Research

16 pages, 4260 KiB  
Article
Convolution Neural Network Based Multi-Label Disease Detection Using Smartphone Captured Tongue Images
by Vibha Bhatnagar and Prashant P. Bansod
Appl. Sci. 2024, 14(10), 4208; https://doi.org/10.3390/app14104208 - 15 May 2024
Viewed by 334
Abstract
Purpose: Tongue image analysis for disease diagnosis is an ancient, traditional, non-invasive diagnostic technique widely used by traditional medicine practitioners. Deep learning-based multi-label disease detection models have tremendous potential for clinical decision support systems because they facilitate preliminary diagnosis. Methods: In this work, [...] Read more.
Purpose: Tongue image analysis for disease diagnosis is an ancient, traditional, non-invasive diagnostic technique widely used by traditional medicine practitioners. Deep learning-based multi-label disease detection models have tremendous potential for clinical decision support systems because they facilitate preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline where observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. Subjects, who consult collaborating physicians, voluntarily provide all images. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolutional neural network (MobileNetV2) model for binary classification. Once it predicts the diseased label, the disease prediction algorithm based on DenseNet-121 uses the image to diagnose single or multiple disease labels. Results: The MobileNetV2 architecture-based disease detection model achieved an average accuracy of 93% in distinguishing between diseased and normal, healthy tongues, whereas the multilabel disease classification model produced more than 90% accurate results for the disease class labels considered, strongly indicating a successful outcome with the smartphone-captured image dataset. Conclusion: AI-based image analysis shows promising results, and an extensive dataset could provide further improvements to this approach. Experimenting with smartphone images opens a great opportunity to provide preliminary health status to individuals at remote locations as well, prior to further treatment and diagnosis, using the concept of telemedicine. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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15 pages, 6993 KiB  
Article
Methodology of Labeling According to 9 Criteria of DSM-5
by Geonju Lee, Dabin Park and Hayoung Oh
Appl. Sci. 2023, 13(18), 10481; https://doi.org/10.3390/app131810481 - 20 Sep 2023
Viewed by 711
Abstract
Depression disorder is a disease that causes a deterioration of daily function and can induce thoughts of suicide. The Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5), which is the official reference of the American Psychiatry Association and is also used [...] Read more.
Depression disorder is a disease that causes a deterioration of daily function and can induce thoughts of suicide. The Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5), which is the official reference of the American Psychiatry Association and is also used in Korea to identify depressive disorders, sets nine criteria for diagnosing depressive disorders. The lack of counseling personnel, including psychiatrists, and negative social perceptions of depressive disorders prevent counselors from being treated for depressive disorders. Natural language processing-based artificial intelligence (AI) services such as chatbots can help fill this need, but labeled datasets are needed to train AI services. In this study we collected data from AI Hub wellness consultations and crawls of the Reddit website to augment and build word dictionaries and analyze morphemes using the Kind Korean Morpheme Analyzer and Word2Vec. The collected datasets were labeled based on word dictionaries built according to nine DSM-5 depressive disorder diagnostic criteria. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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19 pages, 6846 KiB  
Article
A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction
by Meshrif Alruily, Sameh Abd El-Ghany, Ayman Mohamed Mostafa, Mohamed Ezz and A. A. Abd El-Aziz
Appl. Sci. 2023, 13(8), 5047; https://doi.org/10.3390/app13085047 - 18 Apr 2023
Cited by 3 | Viewed by 1874
Abstract
A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the [...] Read more.
A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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