ArticleJAMIA open2020
Adapting and evaluating a deep learning language model for clinical why-question answering.
Article in JAMIA open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Question answering systems for health professionals at the point of care-a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- RadTechBERT: a domain-specific language model for radiological technology evaluated using Japanese national examination-derived cloze questions.Radiological physics and technology · 2026Article
- PMR-Q&A: Development of a Bilingual Expert-Evaluated Question-Answer Dataset for Large Language Models in Physical Medicine and Rehabilitation.Bioengineering (Basel, Switzerland) · 2026Article
- Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.Journal of medical Internet research · 2024Article
- Information extraction from weakly structured radiological reports with natural language queries.European radiology · 2024Article
- Large language models in physical therapy: time to adapt and adept.Frontiers in public health · 2024Article
- Could an artificial intelligence approach to prior authorization be more human?Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A comparative study of pretrained language models for long clinical text.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Drug-disease association prediction with literature based multi-feature fusion.Frontiers in pharmacology · 2023Article
- Applications of natural language processing in ophthalmology: present and future.Frontiers in medicine · 2022Review
- Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo adapt and evaluate a deep learning language model for answering why-questions based on patient-specific clinical text. MATERIALS AND
methodsBidirectional encoder representations from transformers (BERT) models were trained with varying data sources to perform SQuAD 2.0 style why-question answering (why-QA) on clinical notes. The evaluation focused on: (1) comparing the merits from different training data and (2) error analysis.
resultsThe best model achieved an accuracy of 0.707 (or 0.760 by partial match). Training toward customization for the clinical language helped increase 6% in accuracy. DISCUSSION: The error analysis suggested that the model did not really perform deep reasoning and that clinical why-QA might warrant more sophisticated solutions.
conclusionThe BERT model achieved moderate accuracy in clinical why-QA and should benefit from the rapidly evolving technology. Despite the identified limitations, it could serve as a competent proxy for question-driven clinical information extraction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.