ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distance.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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
6 citing papers in PubMed.
- Enhancing Precision in Detecting Severe Immune-Related Adverse Events: Comparative Analysis of Large Language Models and International Classification of Disease Codes in Patient Records.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.JMIR medical informatics · 2024Article
- Advancing entity recognition in biomedicine via instruction tuning of large language models.Bioinformatics (Oxford, England) · 2024Article
- Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning.Bioinformatics (Oxford, England) · 2024Article
- Article
- A cross-institutional evaluation on breast cancer phenotyping NLP algorithms on electronic health records.Computational and structural biotechnology journal · 2023Article
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Authors and funding
4 authors.
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Abstract
Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes generalize variably between institutions, but ignored other levels of healthcare organization. We measured SciBERT diagnosis sentiment classifier generalizability between medical specialties using EHR sentences from MIMIC-III. Models trained on one specialty performed better on internal test sets than mixed or external test sets (mean AUCs 0.92, 0.87, and 0.83, respectively; p = 0.016). When models are trained on more specialties, they have better test performances (p < 1e-4). Model performance on new corpora is directly correlated to the similarity between train and test sentence content (p < 1e-4). Future studies should assess additional axes of generalization to ensure deep learning models fulfil their intended purpose across institutions, specialties, and practices.
Indexed as
Identifiers
34457149PMC8378651What OpenQuestion holds
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.