Transformative Technologies in Healthcare: Harnessing Machine Learning, Deep Learning and Large Language Models in Health Informatics

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 87

Special Issue Editors

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Guest Editor
eHEALTH Lab, College of Communication and Information, Florida State University, Tallahassee, FL 32306-2100, USA
Interests: biomedical informatics; electronic health records; machine learning; natural language processing

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Guest Editor
Section for Biomedical Informatics & Data Science, School of Medicine, Yale University, New Haven, CT 06510, USA
Interests: natural language processing; machine learning; deep learning; GPT; bioinformatics

Special Issue Information

Dear Colleagues,

The integration of machine learning and artificial intelligence in clinical and biomedical natural language processing (NLP) is transforming healthcare by enhancing data management and improving care quality. The level of accuracy reported in some of the tasks enables the models to be integrated into a clinical workflow for automation. Techniques such as deep learning and transformer models are crucial for fundamental tasks including concept extraction, normalization, and relationship extraction and further facilitate the creation of accurate knowledge graphs that support clinical decision-making. AI-driven tools effectively disambiguate clinical abbreviations and extract adverse drug events, significantly improving the accuracy and safety of automated diagnoses. In addition, analyzing unstructured medical data to identify social determinants of health supports more personalized care strategies. The advances in natural language inference and medication attribute filling support nuanced information extraction from medical narratives. Overall, the application of sophisticated AI and NLP techniques in healthcare optimizes both data utilization and patient management, heralding a new era of AI-driven medical innovation.

Topics of interest include (but are not limited to) the following:

  • Clinical/biomedical concept extraction and/or normalization;
  • Clinical/biomedical relation extraction;
  • Clinical/biomedical abbreviation disambiguation;
  • Social determinants of health (SDoH);
  • Adverse drug event extraction;
  • Medication attribute filling;
  • Progress note understanding;
  • Retrospective case-control study;
  • Network analysis.

This Special Issue invites original research that explores the intersection of clinical and biomedical natural language processing (NLP). We are particularly interested in contributions that examine the breadth of extraction tasks, utilizing advanced machine learning, deep learning, and large language models for data extraction processes. Submissions should highlight the diversity of data sources leveraged and the various forms of outputs generated. We encourage a range of research methodologies, including quantitative, qualitative, and mixed methods. Additionally, case studies and reports are welcome, provided that they demonstrate significant impact and offer valuable insights at a scale relevant to our readership.

Dr. Balu Bhasuran
Dr. Kalpana Raja
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Information is an international peer-reviewed open access monthly 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 1600 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.


  • clinical natural language processing
  • biomedical natural language processing
  • machine learning
  • artificial intelligence
  • healthcare applied AI
  • automated diagnosis
  • knowledge graphs
  • prospective study
  • causal models
  • prompt tuning
  • large language model
  • text generation
  • transformer model

Published Papers

This special issue is now open for submission.
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