ArticleIEEE journal of biomedical and health informatics2021
A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data.
Article in IEEE journal of biomedical and health informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Evaluation of trajectory analysis for disease risk assessment: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.Artificial intelligence in medicine · 2024Article
- Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- DETECT: Feature extraction method for disease trajectory modeling in electronic health records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Using sequence clustering to identify clinically relevant subphenotypes in patients with COVID-19 admitted to the intensive care unit.Journal of the American Medical Informatics Association : JAMIA · 2022Article
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Authors and funding
7 authors.
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Abstract
Diseases can show different courses of progression even when patients share the same risk factors. Recent studies have revealed that the use of trajectories, the order in which diseases manifest throughout life, can be predictive of the course of progression. In this study, we propose a novel computational method for learning disease trajectories from EHR data. The proposed method consists of three parts: first, we propose an algorithm for extracting trajectories from EHR data; second, three criteria for filtering trajectories; and third, a likelihood function for assessing the risk of developing a set of outcomes given a trajectory set. We applied our methods to extract a set of disease trajectories from Mayo Clinic EHR data and evaluated it internally based on log-likelihood, which can be interpreted as the trajectories' ability to explain the observed (partial) disease progressions. We then externally evaluated the trajectories on EHR data from an independent health system, M Health Fairview. The proposed algorithm extracted a comprehensive set of disease trajectories that can explain the observed outcomes substantially better than competing methods and the proposed filtering criteria selected a small subset of disease trajectories that are highly interpretable and suffered only a minimal (relative 5%) loss of the ability to explain disease progression in both the internal and external validation.
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