Data Mining and Healthcare Informatics
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 62
Special Issue Editors
Interests: technology uses and adoptions; AI; cybersecurity; business analytics
Interests: data mining; database management systems; neural networks and their use in financial forecasting; database marketing
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the intersection of data mining techniques, biostatistics, and healthcare informatics. With the rapid growth of electronic health records, wearable health technologies, telemedicine platforms, and AI-powered diagnostic systems, there is a critical need for innovative, interpretable, scalable, and ethically responsible analytical methods. These approaches are essential to informing clinical decisions, enhancing patient outcomes, optimizing healthcare delivery, and advancing population health research.
We welcome submissions that cover, but are not limited to, the following topics:
- Development and validation of predictive and prescriptive healthcare analytics;
- Integration of data mining techniques with electronic health records (EHRs) and health information exchange systems;
- Statistical modeling and inference techniques applied to healthcare data;
- Analysis and interpretation of data from wearable devices and remote patient monitoring;
- Application of machine learning models and biostatistical methods for disease prediction, diagnosis, and prognosis;
- Patient-centric analytics aimed at personalized medicine and precision healthcare;
- Novel methodologies in clinical decision support systems leveraging data mining approaches;
- Ethical, privacy, and regulatory considerations in healthcare data mining;
- Frameworks for enhancing interpretability and explainability of AI-driven healthcare analytics;
- Case studies illustrating real-world impact and lessons learned in healthcare data mining implementations;
- Data-driven approaches to population health management and public health surveillance.
This Issue aims to foster collaboration among researchers from disciplines including information systems, health informatics, data science, and bioinformatics. Together, contributors will explore emerging challenges, innovative solutions, and opportunities to leverage data mining and biostatistical techniques for tangible improvements in healthcare and public health outcomes.
Dr. Ning Yang
Prof. Dr. Mary Malliaris
Dr. Jason Parton
Guest Editors
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 submissions that pass pre-check are 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. Information is an international peer-reviewed open access monthly 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 1800 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
- data mining
- healthcare informatics
- predictive analytics
- electronic health records (EHRs)
- wearable technology
- clinical decision support systems
- machine learning in healthcare
- personalized medicine
- population health analytics
- public health surveillance
- health data privacy
- interpretable AI
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