Natural language processing for adjudication of heart failure in the electronic health record
Open accessAbstract
Electronic health record (EHR) data repositories have the potential to accelerate research in heart failure (HF) epidemiology, biology, and policy because of large sample size, long-term follow-up, and granular clinical details.However, imprecise ascertainment of clinical endpoints such as HF hospitalization by International Classification of Disease (ICD) diagnosis codes has limited research using EHR data sets.The gold standard for endpoint adjudication, physician review of medical records, is not feasible at the scale of EHR cohorts.Innovation in natural language processing (NLP) now enables training of complex text models with few labeled examples.In this study, we trained and validated an NLP model for automated adjudication of HF hospitalization from raw discharge summary text.We studied hospitalizations in the Community Care Cohort Project (C3PO), an EHR cohort of 520,868 patients (mean age: 48 years, 61% women) receiving longitudinal primary care between 2001 and 2018 at Mass General Brigham. 1 Analysis of C3PO was approved by the Mass General Brigham Institutional Review Board.Possible HF hospitalizations were identified by ICD codes in the primary or secondary position as defined by the American Heart Association "Get with the Guidelines" registry.Boardcertified cardiologists adjudicated medical records to determine whether hospitalizations met the Standardized Data Collection for Cardiovascular Trials Initiative criteria for HF: 1
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30-40% of hospitalizations with primary ICD codes for HF do not meet established criteria upon medical record review.
“However, these codes can be imprecise: 30 -40 % of hospi talizations with primary ICD codes for HF do not meet the established criteria upon medical record review, such as iatrogenic volume overload following surgery or critical illness, or patients with chronic HF admitted for reasons other than acute HF (AHF) [7 -9].”
From Assessing large language models for acute heart failure classification and information extraction from French clinical notes (2025) · cites this paper as [9]
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Age dependent associations of risk factors with heart failure: pooled population based cohort study
Tromp, J., Paniagua, S. M., Lau, E. S., Allen, N. B., Blaha, M. J., Gansevoort, R. T., Hillege, H. L., Lee, D. S., Levy, D. A., Vasan, R. S., et al. (2021) · BMJ · doi:10.1136/bmj.n461 · Cited by 243 · see citing works
Self-citationCohort design and natural language processing to reduce bias in electronic health records research
Khurshid, S. S., Reeder, C., Harrington, L. X., Singh, P., Sarma, G., Friedman, S. F., Achille, P. D., Diamant, N., Cunningham, J. W., Turner, A. C., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00590-0 · Cited by 80 · see citing works
Self-citationClinical-Longformer and Clinical-BigBird: Transformers for long clinical sequencesPreprint
Li, Y., Wehbe, R. M., Ahmad, F. S., Wang, H., & Luo, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.11838 · Cited by 65 · see citing works
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Cunningham, J. W., Singh, P., Reeder, C., Claggett, B., Marti-Castellote, P. M., Lau, E. S., Khurshid, S., Batra, P., Lubitz, S. A., Maddah, M., et al. (2024) · JAMA Cardiology · doi:10.1001/jamacardio.2023.4859 · Cited by 38 · see citing works
Self-citationBazoge, A., Wargny, M., Constant dit Beaufils, P., Morin, E., Daille, B., Gourraud, P.-A., & Hadjadj, S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110609 · Cited by 6 · see citing works
Artificial Intelligence-Enabled ECG Analysis to Predict Incident Heart Failure
Khurshid, S., Friedman, S. F., Kany, S., D’Souza, V., Roshandelpoor, A., Kosyakovsky, L. B., Chitsazan, M., Cunningham, J. W., Singh, P., Lau, E. S., et al. (2026) · Circulation: Heart Failure · doi:10.1161/circheartfailure.125.013927 · Cited by 2 · see citing works
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- Authors
- Cunningham, J. W., Singh, P., Reeder, C., Lau, E. S., Khurshid, S., Wang, X., Ellinor, P. T., Lubitz, S. A., Batra, P., & Ho, J. E.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Corresponding author
- Ho, Jennifer E.
- ORCID
- Venue
- JACC: Heart Failure
- Publisher
- Elsevier BV
- Published
- 2023-07
- Volume
- 11
- Issue
- 7
- Pages
- 852-854
- ISSN
- 2213-1779 (ISSN-L)
- PMC
- PMC10694785
- OpenAlex
- W4323256948
- Cited by
- 21
- References
- 3
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 7 · 2023: 3
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- JACC Heart Failure (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH
- American Heart Association Inc
- Leducq Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- heart failure, heart failure hospitalization, adjudication, natural language processing, electronic health records, ICD codes, primary care
- MeSH terms
- Heart Failure, Humans, Natural Language Processing, Electronic Health Records
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