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Timeline Registration for Electronic Health Records.

ABSTRACT: Electronic Health Record (EHR) data are captured over time as patients receive care. Accordingly, variations among patients, such as when a patient presents for care during the course of a disease, introduce bias into standard longitudinal EHR data analysis methods. We, therefore, aim to provide an alignment method that reduces this bias. We structure this task as a registration problem. While limited prior research on longitudinal EHR data considered registration, we propose a robust registration method to provide better data alignment by estimating the optimum time shift at each time point. We validate the proposed method for mortality prediction. We utilize a Recurrent Neural Network (RNN), time-varying Cox regression model, and Logistic Regression (LR) for mortality prediction. Results suggest our proposed registration method enhances mortality prediction with at least a 1-2% increase in major evaluation metrics utilized.

SUBMITTER: Jiang S

PROVIDER: S-EPMC10283114 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Timeline Registration for Electronic Health Records.

Jiang Shiyi S Han Rungang R Chakrabarty Krishnendu K Page David D Stead William W WW Zhang Anru R AR

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 20230616

Electronic Health Record (EHR) data are captured over time as patients receive care. Accordingly, variations among patients, such as when a patient presents for care during the course of a disease, introduce bias into standard longitudinal EHR data analysis methods. We, therefore, aim to provide an alignment method that reduces this bias. We structure this task as a registration problem. While limited prior research on longitudinal EHR data considered registration, we propose a robust registrati ...[more]

PMID: 37350882

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