ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- OntoCodex: a multi-agent biomedical ontology enrichment framework.npj health systems · 2026Article
- Automated Review of Patient Records: Privacy-Preserving Large Language Models for Identifying Incident Nonarteritic Anterior Ischemic Optic Neuropathy at Scale.Ophthalmology science · 2026Article
- A simplified retriever to improve accuracy of phenotype normalizations by large language models.Frontiers in digital health · 2025Article
- Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making.Seminars in radiation oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential for realizing value from electronic health records (EHR) in support of precision medicine. Despite technological advances, high-throughput phenotyping remains a challenge. This study compares three computational approaches to high-throughput phenotyping: a large language model (LLM) incorporating generative AI, a deep learning (DL) approach utilizing span categorization, and a machine learning (ML) approach with word embeddings. The LLM approach that implemented GPT-4 demonstrated superior performance, suggesting that large language models are poised to become the preferred method for high-throughput phenotyping ofphysician notes.
Indexed as
Identifiers
40417529PMC12099424What Socratic 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 Socratic graph.