Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure
In this diagnostic study of 34 070 patients with HF, the NLP approach accurately extracted a patient's NYHA symptom class and activity- or rest-related HF symptoms from clinical notes, enhancing the ability to track optimal care delivery and identify patients eligible for clinical trial participation from unstructured documentation.
Authors
- Rohan Khera (ORCID: https://orcid.org/0000-0001-9467-6199)
- Cynthia Brandt (ORCID: https://orcid.org/0000-0001-8179-1796)
- Lovedeep Singh Dhingra (ORCID: https://orcid.org/0000-0002-5664-4126)
- Xinyu Zhou (ORCID: https://orcid.org/0000-0003-0968-4229)
- Philip Adejumo (ORCID: https://orcid.org/0000-0002-9289-8377)
- Arya Aminorroaya (ORCID: https://orcid.org/0000-0003-3197-2657)
- Phyllis Thangaraj (ORCID: https://orcid.org/0000-0003-3968-1563)
- Hua Xu (ORCID: https://orcid.org/0000-0002-5274-4672)
- Harlan M. Krumholz
Institutions
- Yale New Haven Hospital (US)
- Yale University (US)
- VA Connecticut Healthcare System (US)
- Yale New Haven Health System (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2024-11-07
- DOI
- https://doi.org/10.1001/jamanetworkopen.2024.43925
- Citations
- 22
- Primary Topic
- Diabetes Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 7.87