Machine Learning Applications for Risk Stratification in Healthcare

Dear Colleagues,

The rapid advancement of Artificial Intelligence (AI) and machine learning (ML) is transforming the way healthcare systems approach risk stratification and personalized care. Predictive models based on ML enable the analysis of large-scale clinical, biological, and behavioral data, uncovering hidden patterns and supporting professionals in forecasting clinical outcomes, managing resources, and designing preventive and therapeutic strategies.

This Special Issue, titled “Machine Learning Applications for Risk Stratification in Healthcare”, aims to gather original research articles and reviews that explore innovative approaches for clinical risk assessment and prediction. Topics of interest include the development and validation of predictive models, ML-based survival analysis, and the integration of electronic health records and multi-omics data, as well as the implementation of Explainable AI (XAI) methods to ensure transparency and trustworthiness in clinical decision-making.

The goal is to foster interdisciplinary dialog among computer scientists, clinicians, and healthcare policymakers, while highlighting the potential of ML to enhance the quality, efficiency, and equity of healthcare delivery.

Recommended topics include, but are not limited to, the following:

• Development and validation of ML-based predictive models for clinical risk stratification;
• Survival analysis and time-to-event prediction using machine learning;
• Integration of electronic health records, imaging, and multi-omics data for risk prediction;
• Explainable AI (XAI) and interpretable models for trustworthy decision support;
• Deep learning and neural network applications in healthcare risk assessment;
• Handling imbalanced, missing, and heterogeneous healthcare data;
• Clinical case studies and real-world applications of ML for outcome prediction;
• Ethical, legal, and social implications of AI in clinical risk stratification;
• Resource allocation and optimization strategies supported by predictive analytics;
• AI-driven personalized medicine and patient-centered care pathways;
• Comparative analyses of ML algorithms and hybrid modeling approaches;
• Policy and governance frameworks for the implementation of ML in healthcare systems.

Dr. Vito Santamato
Dr. Agostino Marengo
Dr. Massimo Iacoviello
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. AI in Medicine is an international peer-reviewed open access quarterly 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 1000 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.

• machine learning
• risk stratification
• predictive modeling
• clinical decision support
• survival analysis
• explainable AI (XAI)
• healthcare data analytics
• personalized medicine