Applications of Data Mining in Patient Care
A special issue of Healthcare (ISSN 2227-9032).
Deadline for manuscript submissions: 31 October 2026 | Viewed by 1295
Editor
Special Issue Information
Dear Colleagues,
In the realm of patient care, data mining plays a crucial role in extracting valuable insights from complex health data, enabling the development of predictive models, decision support systems, and personalized treatment strategies. By leveraging advanced analytical techniques, data mining facilitates improved clinical decision-making, enhances patient outcomes, and optimizes healthcare resource utilization.
Despite significant advancements in data-driven healthcare, challenges remain in relation to ensuring data quality and interoperability and factoring in ethical considerations in patient data mining. The integration of artificial intelligence, machine learning, and natural language processing presents new opportunities for enhancing the accuracy, efficiency, and applicability of data-driven healthcare solutions.
This Special Issue will explore innovative applications of data mining in patient care, addressing key challenges and emerging opportunities in this field. We invite researchers, clinicians, and industry professionals to contribute high-quality original research articles and review papers that provide novel insights and evidence-based recommendations for the application of data mining techniques in healthcare.
Topics of interest for this Special Issue include, but are not limited to, the following:
- Systematic reviews and meta-analyses of data mining applications in patient care;
- The development of digital biomarkers for chronic disease management;
- Validation studies on machine learning models for clinical decision support;
- Psychometric analyses of health data mining approaches;
- Comparative studies on different data mining techniques for patient outcome prediction;
- Cross-sectional studies examining the cultural adaptation of data-driven healthcare tools;
- The integration of artificial intelligence and natural language processing into patient care;
- Predictive analytics for early detection and intervention in frailty and chronic diseases;
- Data-driven approaches to chronic disease (hypertension, diabetes, etc.) risk assessment;
- Ethical considerations and regulatory challenges in data-driven healthcare.
I look forward to receiving your contributions.
Dr. Catherine Park
Guest Editor
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare 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 2700 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
- machine learning
- artificial intelligence
- clinical decision support systems
- digital biomarkers
- predictive analytics
- big data analysis
- electronic health records
- frailty
- chronic disease
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