Multimodal Learning and Knowledge Extraction for Precision Healthcare: Diagnosis, Risk Prediction, and Clinical Decision Support

Dear Colleagues,

The proposed Special Issue will address emerging advances in machine learning and knowledge extraction for precision healthcare, with particular emphasis on integrating heterogeneous biomedical and clinical data. Modern healthcare systems generate medical images, clinical text, electronic health records, physiological signals, pathology data, genomics, and information from wearable devices. However, extracting clinically meaningful and generalizable knowledge from these complementary data sources remains a major methodological and translational challenge.The Special Issue will provide an interdisciplinary platform for research on multimodal data fusion, representation learning, medical foundation models, vision–language models, large language models, explainable and causal artificial intelligence, uncertainty estimation, federated learning, and privacy-preserving analytics. Relevant applications will include disease diagnosis, prognosis, risk prediction, patient stratification, treatment-response assessment, and clinical decision support across oncology, cardiovascular disease, neurology, mental health, musculoskeletal disorders, and other healthcare domains. Particular attention will be given to methodological rigor, interpretability, fairness, external validation, reproducibility, and real-world clinical translation. The proposed topic closely aligns with MAKE’s focus on machine learning methods that transform complex data into useful and actionable knowledge.

Dr. Hazrat Ali
Dr. Sadam Hussain
Prof. Dr. Jan Baumbach
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access monthly journal published by MDPI.

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