Subtype-Aware Registration of Longitudinal Electronic Health Records

TL;DR

This paper introduces a subtype-aware timeline registration method for EHR data that corrects timeline misalignments, improving disease subtyping and downstream clinical analysis accuracy.

Contribution

The paper presents a novel data projection and discrete optimization approach for aligning longitudinal EHR records considering disease subtypes.

Findings

Effective alignment of distorted records with true disease progression

Enhanced disease subtyping clarity

Improved downstream clinical analysis performance

Abstract

Electronic Health Records (EHRs) contain extensive patient information that can inform downstream clinical decisions, such as mortality prediction, disease phenotyping, and disease onset prediction. A key challenge in EHR data analysis is the temporal gap between when a condition is first recorded and its actual onset time. Such timeline misalignment can lead to artificially distinct biomarker trends among patients with similar disease progression, undermining the reliability of downstream analyses and complicating tasks such as disease subtyping and outcome prediction. To address this challenge, we provide a subtype-aware timeline registration method that leverages data projection and discrete optimization to correct timeline misalignment. Through simulation and real-world data analyses, we demonstrate that the proposed method effectively aligns distorted observed records with the true disease progression patterns, enhancing subtyping clarity and improving performance in downstream clinical analyses.