Estimating Disease Onset Time by Modeling Lab Result Trajectories via Bayes Networks Article Swipe

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Won-Suk Oh , Pranjul Yadav , Vipin Kumar , Pedro J. Caraballo , M. Regina Castro , Michael Steinbach , György Simon ·

YOU? · · 2017 · Open Access · · DOI: https://doi.org/10.1109/ichi.2017.41

The true onset time of a disease, particularly slow-onset diseases like Type 2 diabetes mellitus (T2DM), is rarely observable in electronic health records (EHRs). However, it is critical for analysis of time to events and for studying sequences of diseases. The aim of this study is to demonstrate a method for estimating the onset time of such diseases from intermittently observable laboratory results in the specific context of T2DM. A retrospective observational study design is used. A cohort of 5,874 non-diabetic patients from a large healthcare system in the Upper Midwest United States was constructed with a three-year follow-up period. The HbA1c level of each patient was collected from earliest and the latest follow-up. We modeled the patients' HbA1c trajectories through Bayesian networks to estimate the onset time of diabetes. Due to non-random censoring and interventions unobservable from EHR data (such as lifestyle changes), naïve modeling of HbA1c through linear regression or modeling time-to-event through proportional hazard model leads to a clinically infeasible model with no or limited ability to predict the onset time of diabetes. Our model is consistent with clinical knowledge and estimated the onset of diabetes with less than a six-month error for almost half the patients for whom the onset time could be clinically ascertained. To our knowledge, this is the first study of modeling long-term HbA1c progression in non-diabetic patients and estimating the onset time of diabetes.

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Artificial Intelligence

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Censoring (clinical trials) Proportional hazards model Unobservable Medicine Observational study Context (archaeology) Disease Diabetes mellitus Retrospective cohort study Bayes' theorem Bayesian probability Statistics Computer science Econometrics Internal medicine Artificial intelligence Mathematics Endocrinology Biology Paleontology Pathology

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/ichi.2017.41
OA Status: green
Cited By: 3
References: 27
Related Works: 10
OpenAlex ID: https://openalex.org/W2756256156

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DOI: https://doi.org/10.1109/ichi.2017.41

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Title: Estimating Disease Onset Time by Modeling Lab Result Trajectories via Bayes Networks

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Language: en

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Publication year: 2017

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Publication date: 2017-08-01

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Authors: Won-Suk Oh, Pranjul Yadav, Vipin Kumar, Pedro J. Caraballo, M. Regina Castro, Michael Steinbach, György Simon

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Landing page: https://doi.org/10.1109/ichi.2017.41

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OA URL: https://www.ncbi.nlm.nih.gov/pmc/articles/5975351

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Concepts: Censoring (clinical trials), Proportional hazards model, Unobservable, Medicine, Observational study, Context (archaeology), Disease, Diabetes mellitus, Retrospective cohort study, Bayes' theorem, Bayesian probability, Statistics, Computer science, Econometrics, Internal medicine, Artificial intelligence, Mathematics, Endocrinology, Biology, Paleontology, Pathology

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Cited by: 3

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