ArticleGenomics & informatics2024
Towards automated phenotype definition extraction using large language models.
Article in Genomics & informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A generative approach for semantic auditing of electronic health records.NPJ digital medicine · 2026Article
- SHREC: A framework for advancing next-generation computational phenotyping with large language models.PLOS digital health · 2026Article
- Phecoder: semantic retrieval for auditing and expanding ICD-based phenotypes in EHR biobanks.medRxiv : the preprint server for health sciences · 2026Article
- SHREC: A Framework for Advancing Next-Generation Computational Phenotyping with Large Language Models.ArXiv · 2025Article
- Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models.Proceedings of machine learning research · 2025Article
- Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks.ArXiv · 2025Article
- PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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Authors and funding
2 authors.
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Abstract
Electronic phenotyping involves a detailed analysis of both structured and unstructured data, employing rule-based methods, machine learning, natural language processing, and hybrid approaches. Currently, the development of accurate phenotype definitions demands extensive literature reviews and clinical experts, rendering the process time-consuming and inherently unscalable. Large language models offer a promising avenue for automating phenotype definition extraction but come with significant drawbacks, including reliability issues, the tendency to generate non-factual data ("hallucinations"), misleading results, and potential harm. To address these challenges, our study embarked on two key objectives: (1) defining a standard evaluation set to ensure large language models outputs are both useful and reliable and (2) evaluating various prompting approaches to extract phenotype definitions from large language models, assessing them with our established evaluation task. Our findings reveal promising results that still require human evaluation and validation for this task. However, enhanced phenotype extraction is possible, reducing the amount of time spent in literature review and evaluation.
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Registered trials
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