LATTE: Label-efficient incident phenotyping from longitudinal electronic health records

Jun Wen, Jue Hou, Clara Lea Bonzel, Yihan Zhao, Victor M. Castro, Vivian S. Gainer, Dana Weisenfeld, Tianrun Cai, Yuk Lam Ho, Vidul A. Panickan, Lauren Costa, Chuan Hong, J. Michael Gaziano, Katherine P. Liao, Junwei Lu, Kelly Cho, Tianxi Cai

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number100906
Issue number1
StatePublished - Jan 12 2024

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  • DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem

PubMed: MeSH publication types

  • Journal Article


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