This paper is in the following e-collection/theme issue:

Clinical Informatics (1325) Clinical Information and Decision Making (1915) Electronic Health Records (1319) Natural Language Processing (917) Big Data (307) Information Retrieval (348) eHealth Infrastructures (427)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53636, first published .
Question Answering for Electronic Health Records: Scoping Review of Datasets and Models

Question Answering for Electronic Health Records: Scoping Review of Datasets and Models

Question Answering for Electronic Health Records: Scoping Review of Datasets and Models

Authors of this article:

Jayetri Bardhan1 Author Orcid Image ;   Kirk Roberts2 Author Orcid Image ;   Daisy Zhe Wang1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Yang H, Li M, Zhou H, Xiao Y, Fang Q, Zhou S, Zhang R. Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study. Journal of Medical Internet Research 2025;27:e70080 View
  2. Salehin I, Tomal Ahmed Sajib M, Huda Badhon N, Sakibul Hassan Rifat M, Amin N, Nessa Moon N. Systematic Literature Review of LLM‐Large Language Model in Medical: Digital Health, Technology and Applications. Engineering Reports 2025;7(9) View
  3. da Silva R, Pazin-Filho A. The incremental value of unstructured data via natural language processing in machine learning-based COVID-19 mortality prediction: a comparative study. BMC Medical Informatics and Decision Making 2025;25(1) View