ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Clinical Information Extraction with Large Language Models: A Case Study on Organ Procurement.
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 7 papers.
What it found
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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
7 citing papers in PubMed.
- Augmenting structured diagnoses through effective use of pre-trained large language models on clinical notes.JAMIA open · 2026Article
- Automated Review of Patient Records: Privacy-Preserving Large Language Models for Identifying Incident Nonarteritic Anterior Ischemic Optic Neuropathy at Scale.Ophthalmology science · 2026Article
- Korean Medical Consultation With Open-Weight Large Language Models: Pilot Comparative Evaluation of Retrieval-Augmented Generation With Metadata Filtering.JMIR formative research · 2026Article
- Predicting emergency department disposition using machine learning and large language models to support proactive capacity management: a multicenter retrospective study.BMC emergency medicine · 2026Article
- ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Large language model derived regular expressions for sleep phenotyping from electronic health record: a feasibility study.Sleep advances : a journal of the Sleep Research Society · 2026Article
- AI-augmented frameworks for enhancing Alzheimer's disease clinical trials: A memory clinic perspective.The journal of prevention of Alzheimer's disease · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent work has demonstrated that large language models (LLMs) are powerful tools for clinical information extraction from unstructured text. However, existing approaches have largely ignored the extraction of numeric information such as laboratory tests and vital signs. In this article, we present a case study on organ procurement that evaluates the ability of LLMs to extract numeric data from clinical text. We first describe our LLM-based approach, introducing a prompting strategy for numeric extraction and novel heuristics to combat hallucination. We validate our approach on a hand-annotated set of 298 notes, demonstrating that it has high accuracy, precision and recall. We then highlight the value of our approach for downstream data analysis using a corpus of 43,719 notes on 14,342 potential organ donors. This case study is a key component of an ongoing collaboration that aims to make data on organ procurement publicly available for informatics research.
Indexed as
Identifiers
40417525PMC12099322What 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.