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Wen J, Hou J, Bonzel CL, Zhao Y, Castro VM, Gainer VS, Weisenfeld D, Cai T, Ho YL, Panickan VA, Costa L, Hong C, Gaziano JM, Liao KP, Lu J, Cho K, Cai T. LATTE: Label-efficient incident phenotyping from longitudinal electronic health records. PATTERNS (NEW YORK, N.Y.) 2024; 5:100906. [PMID: 38264714 PMCID: PMC10801250 DOI: 10.1016/j.patter.2023.100906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/06/2023] [Accepted: 12/01/2023] [Indexed: 01/25/2024]
Abstract
Electronic health record (EHR) data are increasingly used to support real-world evidence studies but are limited by the lack of precise timings of clinical events. Here, we propose a label-efficient incident phenotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data. By leveraging the pre-trained semantic embeddings, LATTE selects predictive features and compresses their information into longitudinal visit embeddings through visit attention learning. LATTE models the sequential dependency between the target event and visit embeddings to derive the timings. To improve label efficiency, LATTE constructs longitudinal silver-standard labels from unlabeled patients to perform semi-supervised training. LATTE is evaluated on the onset of type 2 diabetes, heart failure, and relapses of multiple sclerosis. LATTE consistently achieves substantial improvements over benchmark methods while providing high prediction interpretability. The event timings are shown to help discover risk factors of heart failure among patients with rheumatoid arthritis.
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Affiliation(s)
- Jun Wen
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Jue Hou
- University of Minnesota, Minneapolis, MN, USA
| | - Clara-Lea Bonzel
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | | | | | | | | | - Tianrun Cai
- VA Boston Healthcare System, Boston, MA, USA
- Mass General Brigham, Boston, MA, USA
| | - Yuk-Lam Ho
- VA Boston Healthcare System, Boston, MA, USA
| | - Vidul A. Panickan
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | | | | | - J. Michael Gaziano
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Katherine P. Liao
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Junwei Lu
- VA Boston Healthcare System, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kelly Cho
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Tianxi Cai
- Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
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