Extracting International Classification of Diseases Codes from Clinical Documentation Using Large Language Models
Current LLMs have poor performance in extraction of ICD-10-CM codes from inpatient notes when compared against the human coder.
Authors
- Justin Kauffman
- Donald U. Apakama (ORCID: https://orcid.org/0000-0001-6217-1620)
- Kullaya Takkavatakarn (ORCID: https://orcid.org/0000-0002-6236-8893)
- Gordon S. Smith (ORCID: https://orcid.org/0000-0002-2911-3071)
- Eyal Klang (ORCID: https://orcid.org/0000-0002-4567-3108)
- Jami Pincavitch (ORCID: https://orcid.org/0000-0003-1701-0005)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Brian Dilcher (ORCID: https://orcid.org/0000-0002-9171-7597)
- Robert Freeman (ORCID: https://orcid.org/0000-0003-4946-6533)
- Ankit Sakhuja (ORCID: https://orcid.org/0000-0002-2045-518X)
- Roopa Kohli‐Seth (ORCID: https://orcid.org/0000-0002-6082-1359)
- Ali Soroush (ORCID: https://orcid.org/0000-0001-6900-5596)
- Rebecca Wig
- Alexander W. Charney (ORCID: https://orcid.org/0000-0001-8135-6858)
- Megan McDougal
- Ashley Simmons
- L. Meadows
Institutions
- West Virginia University (US)
- University of Arizona (US)
- Emory University (US)
- Chulalongkorn University (TH)
- King Chulalongkorn Memorial Hospital (TH)
- Mount Sinai Hospital (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Applied Clinical Informatics
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1055/a-2491-3872
- Citations
- 13
- Primary Topic
- Medical Coding and Health Information
- Type
- article
- Field-Weighted Citation Impact
- 9.20
Funders
- National Institute of Diabetes and Digestive and Kidney Diseases