Previously submitted to: JMIR Medical Informatics (no longer under consideration since May 17, 2021)
Date Submitted: May 3, 2021
Open Peer Review Period: May 3, 2021 - Jun 28, 2021
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Using an Optimized Generative Model to Infer the Progression of Complications in Type 2 Diabetes Patients
ABSTRACT
Background:
People with type 2 diabetes have few symptoms, if any, and don’t discover their condition until complications develop. However, little is known about the progression of complications in type 2 diabetes due to data defects in electronic health records (e.g., incomplete records, discrete observation, irregular visits, and progression heterogeneity).
Objective:
The aim of this study was to optimize a generative model to infer the stage of onset of associated complications in patients with type 2 diabetes.
Methods:
Our study utilized real world longitudinal electronic health record (EHR) data from 9,298 patients across an 11-year timespan from a 17-hospital-based regional healthcare delivery network in Shanghai, China who were diagnosed with type 2 diabetes or prediabetic. We used an optimized generative Markov-Bayesian-based model to generate 5000 synthetic illness trajectories, which were manually reviewed by endocrinologists.
Results:
Our optimizations using anchor information to set model parameters perfected highly sparse, noisy irregular, and over discrete EHRs, to be a specified number of entire illness trajectories coped with diabetes-related complications. Given a target stage, it is straightforward to infer the risks of any complications at other stages, not merely transitioning from an earlier state to a later state but from a later state to an earlier state.
Conclusions:
Synthetic patient trajectories simulated by the generative model can counter a lack of real world evidence of desired longitudinal timeframe while offering a strong level of privacy through a lower risk of identifying real patients.
Citation
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