Data Science and Machine Learning in Chronic Disease Management
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Communication and Informatics".
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 407
Special Issue Editors
Interests: health informatics; artificial intelligence; data science; complex networks; project analytics
Special Issues, Collections and Topics in MDPI journals
Interests: data mining; machine learning in health; computational finance; corporate governance; finance education
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We would like to invite papers to this Special Issue of the International Journal of Environmental Research and Public Health, which will explore the application of machine learning, data mining, artificial intelligence or related areas for chronic disease diagnostics, modelling and management.
With the advance of computational technologies, data have become largely available concerning various aspects of disease diagnostics and management. Medical practitioners and allied stakeholders are increasingly looking for innovative ways of visualising and analysing data towards medical diagnostics and managing their patients sustaining chronic and other diseases. Policy makers are aiming at preventing chronic diseases and decreasing the associated economic burdens. Public health officials are seeking information for general well-being of the community. Further, with the current COVID-19 outbreak, it has become apparent infectious diseases can impact human lives and quality of living rapidly from various perspectives—clinical, social and economic. It has become more important than ever to predict and diagnose progression of infectious disease, and the macro and microeconomic impacts and environmental and other risks that can result from such an outbreak.
Machine learning, artificial intelligence and associated technologies can assist in processing big data for automated diagnostics, prediction and intelligent decision making concerning chronic and infectious diseases. The associated computing technologies, including image analysis, network diagnostics and bioinformatics, can also provide useful information for general practitioners, specialists, hospitals, radiology and image providers, pathology service providers, pharmacies, aged care facilities, public health policy makers and funders to work together towards streamlining the healthcare management.
In this Special Issue, we welcome the submission of methodological, empirical and review papers that use machine learning, artificial intelligence and associated technologies and have a clear implication for healthcare decision-making, economic impact management and medical decision making for chronic and infectious diseases. The submitted papers can be based on primary (e.g., based on study design) and/or secondary research data (e.g., administrative claim data and electronic medical records).
Papers of a high academic standard addressing any chronic and infectious disease management issues using machine learning and/or data science are invited for submission to this Special Issue.
Possible topics for this Special Issue include (but are not limited to):
- Expert systems and intelligent systems for chronic and infectious disease diagnostics;
- Intelligent systems for management of chronic and infectious diseases;
- Economic impact management of chronic and infectious diseases using machine learning/AI and associated computing technologies;
- Network analytics and intelligent diagnostics for disease progression and contact tracing;
- Healthcare policy related to chronic and infectious disease
Dr. Shahadat Uddin
Dr. Tasadduq Imam
Guest Editors
Manuscript Submission Information
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