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

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

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• 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