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A Systematic Review of Topic Modeling Techniques for Electronic Health Records.

Iqra Mehmood1, Zoya Zahra1, Sarah Iqbal2

  • 1Department of Computer and Information Sciences, PIEAS, Lehtrar Road, Nilore, Islamabad 45650, Pakistan.

Healthcare (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

Topic modeling effectively analyzes Electronic Health Records (EHRs), extracting valuable clinical insights. This review synthesizes advancements in temporal topic modeling for EHR data, highlighting future potential with AI integration.

Keywords:
Electronic Health RecordsPRISMASystematic Literature Reviewclinical pathway analysispatient trajectoriestopic modeling

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Area of Science:

  • Health Informatics
  • Computational Linguistics
  • Data Science

Background:

  • Electronic Health Records (EHRs) offer rich clinical data but present analytical challenges due to scale, heterogeneity, and temporal complexity.
  • Topic modeling, particularly advanced neural and transformer-based methods like BERTopic, enhances the extraction of latent structures and patient trajectories from EHRs.
  • Traditional probabilistic models and newer neural embedding techniques are being adapted for EHR analysis.

Purpose of the Study:

  • To conduct a Systematic Literature Review (SLR) of topic modeling techniques applied to EHR data over the last decade.
  • To analyze trends in publication, dataset usage, application domains, and methodological advancements in EHR topic modeling.
  • To identify strengths and challenges of various topic modeling approaches in the context of EHR data.

Main Methods:

  • Adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework for study selection.
  • Review of topic modeling techniques including probabilistic models, neural embedding methods, and temporal extensions.
  • Analysis of studies focusing on pathway and sequence modeling within clinical data.

Main Results:

  • Synthesis of publication trends, common datasets, and diverse application areas of topic modeling in EHRs.
  • Identification of strengths in coherence, scalability, and domain adaptability across different modeling families.
  • Highlighting persistent challenges in scalability, interpretability, temporal complexity, and data privacy for large-scale EHR analysis.

Conclusions:

  • Topic modeling remains crucial for uncovering temporal patterns and latent structures within EHR data.
  • Future directions include integrating topic modeling with Agentic AI and large language models to improve clinical decision-making.
  • This SLR provides a foundational resource for researchers and practitioners in temporal topic modeling for advancing data-driven healthcare.