Bayesian analysis for imbalanced positive-unlabelled diagnosis codes in electronic health records
Ru Wang, Ye Liang, Zhuqi Miao, Tieming Liu
The Annals of Applied Statistics · 2023 · Volume 17 · Issue 2
Abstract
With the increasing availability of electronic health records (EHR), significant progress has been made on developing predictive inference and algorithms by health-data analysts and researchers. However, the EHR data are notoriously noisy, due to missing and inaccurate inputs, despite abundant information. One serious problem is that only a small portion of patients in the database has confirmatory diagnoses, while many other patients remain undiagnosed because they did not comply with the recommended examinations. The phenomenon leads to a so-called positive-unlabelled situation, and the labels are extremely imbalanced. In this paper we propose a model-based approach to classify the unlabelled patients by using a Bayesian finite mixture model. We also discuss the label switching issue for the imbalanced data and propose a consensus Monte Carlo approach to address the imbalance issue and improve computational efficiency simultaneously. Simulation studies show that our proposed model-based approach outperforms existing positive-unlabelled learning algorithms. The proposed method is applied on the Cerner EHR for detecting diabetic retinopathy (DR) patients using laboratory measurements. With only 3% confirmatory diagnoses in the EHR database, we estimate the actual DR prevalence to be 25% which coincides with reported findings in the medical literature.
Keywords
classification, consensus Monte Carlo, diabetic retinopathy, multivariate t mixture
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