Special Issue "Disease Prediction, Machine Learning, and Healthcare"
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences & Services".
Deadline for manuscript submissions: 31 December 2021.
2. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
3. Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: big data and databases; data mining; biomedical informatics; and bioinformatics; deep learning and interdisciplinary applications
Special Issues and Collections in MDPI journals
The goal of this Special Issue is to explore how emerging technology solutions and systems in disease and healthcare applications can help human beings to lead heathy lives. Specifically, innovative contributions that either solve or advance the understanding of issues related to new technologies and applications in the real world are very welcome.
This Special Issue also seeks to not only bring solutions that combine state-of-the-art prediction methods for exploiting the huge health and bio data resources available (while ensuring that these systems are explainable to domain experts), but also emerging methods that more generally describe the successful application of AI and big data analytic methodologies to issues such as disease prediction, machine learning, deep learning, knowledge discovery, big data, and feature selection in the medical domain as well as healthcare, biology, and wellbeing domains. The main idea is to cover health data analytics issues addressing all facets of the solutions from the disease prediction and healthcare technology perspective.
The general idea behind this Special Issue is to disseminate disease prediction and healthcare solution contributions from various engineering, scientific, and social settings that exploit data analytics, machine learning, and data mining techniques.
This Special Issue will include papers that span a wide range of topics in the fields of applied medical informatics, healthcare, bioinformatics, and data analytics, ranging from methodological aspects to theoretical and technological views. More specifically, this Special Issue covers some emerging and real-world applicable research topics concerning new trends in applied data analytics, such as machine learning, deep learning, knowledge discovery, feature selection, data analytics, big data platform-related disease prediction and healthcare, and medical data analytics.
A variety of modern real-life settings along with academic and industrial contexts could benefit from the dissemination of these advances and novel paradigms covering all facets of the data discovery process. Industries and modern applications could share their experience in exploiting medical and healthcare solutions keeping pace with the latest technologies. Academics could identify open research issues coming from the industrial and real-life contexts to continuously support the methodological and technological solutions.
TOPICS OF INTEREST
This Special Issue welcomes the submission of technical, experimental, methodological, and data analytical contributions focused on real-world problems and systems, as well as on general applications of AI and big data analytic methodologies in medical Informatics, bioinformatics, medical and health data, and healthcare applications, including but not limited to the following topics:
- Disease prediction methods and techniques;
- Data mining and knowledge discovery in healthcare;
- Machine and deep learning approaches for disease and health data;
- Decision support systems for healthcare and wellbeing;
- Optimization for healthcare problems;
- Regression and forecasting for medical and/or biomedical signals;
- Healthcare information systems;
- Wellness information systems;
- Medical signal and image processing and techniques;
- Medical expert systems;
- Biomedical applications;
- Applications of AI techniques in healthcare and wellbeing systems;
- Machine learning-based medical systems;
- Medical data and knowledge bases;
- Neural networks in medical applications;
- Intelligent computing and platforms in medicine and healthcare;
- Biomedical text mining;
- Deep learning and methods to explain disease prediction;
- Big data frameworks and architectures for applied medical and health data;
- Visualization and interactive interfaces related to healthcare systems.
Dr. Keun Ho Ryu
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 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.
- disease prediction
- machine learning
- deep learning
- big data
- data analytics
- medical and health data