1. Retrospective comparison of traditional and artificial intelligence-based heart failure phenotyping in a US health system to enable real-world evidence
- Author
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Arthur Reshad Garan, Keri L Monda, Ricardo E Dent-Acosta, Daniel J Riskin, and Ty J Gluckman
- Subjects
Medicine - Abstract
Objective Quantitatively evaluate the quality of data underlying real-world evidence (RWE) in heart failure (HF).Design Retrospective comparison of accuracy in identifying patients with HF and phenotypic information was made using traditional (ie, structured query language applied to structured electronic health record (EHR) data) and advanced (ie, artificial intelligence (AI) applied to unstructured EHR data) RWE approaches. The performance of each approach was measured by the harmonic mean of precision and recall (F1 score) using manual annotation of medical records as a reference standard.Setting EHR data from a large academic healthcare system in North America between 2015 and 2019, with an expected catchment of approximately 5 00 000 patients.Population 4288 encounters for 1155 patients aged 18–85 years, with 472 patients identified as having HF.Outcome measures HF and associated concepts, such as comorbidities, left ventricular ejection fraction, and selected medications.Results The average F1 scores across 19 HF-specific concepts were 49.0% and 94.1% for the traditional and advanced approaches, respectively (p
- Published
- 2023
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