Large language models for data extraction from unstructured and semi-structured electronic health records: a multiple model performance evaluation
Objectives We aimed to evaluate the performance of multiple large language models (LLMs) in data extraction from unstructured and semi-structured electronic health records.Methods 50 synthetic medical notes in English, containing a structured and an unstructured part, were drafted and evaluated by d...
Saved in:
| Main Authors: | Vasileios Ntinopoulos, Hector Rodriguez Cetina Biefer, Igor Tudorache, Nestoras Papadopoulos, Dragan Odavic, Petar Risteski, Achim Haeussler, Omer Dzemali |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2025-02-01
|
| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/32/1/e101139.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Advancements in less-invasive aortic root, ascending aorta and arch surgery: current evidence and future directions
by: Laura Rings, et al.
Published: (2025-07-01) -
Modified transventricular and transaortic mitral valve edge-to-edge repair mimicking MitraClip overcorrectionCentral MessagePerspective
by: Nestoras Papadopoulos, MD, PhD, et al.
Published: (2022-04-01) -
Unstructured Electronic Health Records of Dysphagic Patients Analyzed by Large Language Models
by: Luisa Neubig, et al.
Published: (2025-01-01) -
Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
by: Zhuoran Wang, et al.
Published: (2012-01-01) -
Transvenous ICD Implantation into a Coronary Sinus Branch: A Safe and Feasible Alternative to Deliver ICD after Tricuspid Valve Reconstruction
by: M. Gruszczynski, et al.
Published: (2023-01-01)