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Integrating Mathematical Modeling and Data Analysis in Personalized Medical Research

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 653

Special Issue Editors


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Guest Editor
Department of Radiological Informatics and Statistics, Medical University of Gdańsk, 80-210 Gdańsk, Poland
Interests: bioinformatics; graphical representations of biological sequences; biophysics; mathematical modeling in biomedical and social sciences; health and biomedical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue invites authors to submit manuscripts to either

  • Journal of Personalized Medicine (ISSN 2075-4426) for topics related to personalized medicine.
  • Applied Sciences (ISSN 2076-3417) for articles addressing biosciences or bioengineering applications in human health and disease research.

The increasing prominence of mathematical modeling, computational methods, and data analysis in health sciences has attracted significant attention from both biomedical researchers and computer scientists. However, there is often a disconnect between these fields: computer scientists and experts in mathematical modeling may lack awareness of health problems that could benefit from their expertise, while medical researchers may not always recognize mathematics as a potential source of solutions to their challenges. This gap presents an opportunity to expand the application of computational techniques in health sciences, particularly in personalized medicine.

Future developments could focus on creating more accurate methods and introducing innovative approaches that reveal unexpected or previously unknown insights into pressing health issues. The goal of this Special Issue is to explore and expand the intersection of mathematics and health sciences, fostering collaboration and mutual enrichment between these fields, with a particular emphasis on personalized medicine.

We invite submissions to this joint Special Issue, including original research articles, reviews, or hybrid original-review papers. Topics of interest include the following:

  • Novel computational methods or algorithms in personalized medicine.
  • Discussions of the role and significance of mathematical approaches in addressing health-related challenges, with a focus on personalized healthcare.
  • Medical data analysis, including graphical and numerical data representations, tailored to individual patient profiles.
  • Applications of computational methods in various branches of medicine, bioinformatics, public health, physical education, or quality-of-life research, emphasizing personalized treatment strategies.

By integrating mathematical modeling and data processing with personalized medicine, this Special Issue aims to advance the development of individualized healthcare solutions.

You may choose our Joint Special Issue in JPM.

Prof. Dr. Dorota Bielińska-Wąż
Prof. Dr. Piotr Wąż
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 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 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

  • personalized medicine
  • health informatics
  • mathematical modeling
  • data analysis
  • bioinformatics
  • artificial intelligence
  • machine learning

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Published Papers (1 paper)

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Research

22 pages, 1000 KB  
Article
Artificial Intelligence in Questionnaire-Based Research: Quality of Life Classification Across Different Population Groups
by Piotr Wąż, Dorota Bielińska-Wąż and Agnieszka Bielińska-Kaczmarek
Appl. Sci. 2025, 15(24), 13123; https://doi.org/10.3390/app152413123 - 13 Dec 2025
Viewed by 403
Abstract
This interdisciplinary study presents a novel questionnaire analysis methodology using Artificial Intelligence (AI) and Machine Learning (ML). The framework is broadly applicable to all areas of research using questionnaire data analysis, including health sciences and physical education. Our predictive modeling was based on [...] Read more.
This interdisciplinary study presents a novel questionnaire analysis methodology using Artificial Intelligence (AI) and Machine Learning (ML). The framework is broadly applicable to all areas of research using questionnaire data analysis, including health sciences and physical education. Our predictive modeling was based on the XG-Boost algorithm, which classified individuals into three distinct groups—employees and two cohorts of retirees—based on their demographic profiles and responses to the WHOQOL-BREF survey. In order to ensure the credibility and reliability of the predictions, the model building process used the implementation of cross-validation. This procedure produced a model with a resultant accuracy of 0.8038 (95% confidence interval: 0.7551–0.8908). To go beyond conventional performance metrics, we implemented the SHapley Additive exPlanations (SHAP) method, providing a transparent and detailed interpretation of the model’s decision-making process. This explainable AI analysis clarifies both the magnitude and direction of the impact of key factors such as age and various predictors of quality of life, providing detailed, data-driven insights into what differentiates groups. Full article
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