Machine Learning for Biomedical and Clinical Informatics: From Data-Driven Methods to Translational Medical Research
A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 299
Editors
Interests: biomedical informatics; clinical data science; electronic health records; machine learning; natural language processing; large language models; causal inference
Special Issues, Collections and Topics in MDPI journals
Interests: natural language processing; machine learning; deep learning; gpt; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in machine learning (ML) and artificial intelligence (AI) are rapidly transforming biomedical and clinical informatics by enabling scalable analysis of complex, high-dimensional health data. This Special Issue focuses on methodological innovations and real-world applications of ML and AI that advance medical research, clinical decision-making, and population health. Emphasis is placed on informatics-driven approaches that integrate heterogeneous data sources, including electronic health records (EHRs), clinical narratives, biomedical literature, medical imaging, laboratory results, genomics, and other multi-omics data.
We invite original research, reviews, and methodological studies addressing both foundational and applied aspects of biomedical and clinical informatics. Topics of interest include, but are not limited to, predictive modeling, deep learning, natural language processing, large language models, causal inference, explainable AI, and fairness-aware modeling in healthcare. Studies demonstrating translational impact, such as improving diagnosis, prognosis, treatment selection, risk stratification, clinical workflow optimization, and patient-centered decision support, are particularly encouraged.
This Special Issue aims to bridge the gap between algorithmic development and clinical utility by highlighting reproducible, interpretable, and ethically responsible ML approaches. By bringing together interdisciplinary perspectives from informatics, medicine, data science, and public health, this Special Issue seeks to advance the responsible adoption of AI-driven methods in biomedical and clinical research.
Dr. Balu Bhasuran
Dr. Kalpana Raja
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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
- biomedical informatics
- translational medical research
- clinical informatics
- causal inference in health data
- machine learning in healthcare
- electronic health records (EHR)
- natural language processing
- large language models
- artificial intelligence in medicine
- explainable and trustworthy AI
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