Subtype-Aware Registration of Longitudinal Electronic Health Records

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. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.电子健康记录（Electronic Health Records，EHRs）包含详尽的患者信息，可为下游临床决策提供参考依据，例如死亡风险预测、疾病表型分型以及疾病发病预测。电子健康记录数据分析领域的核心挑战之一，在于疾病首次被记录的时间与其实际发病时间之间存在时间错位。此类时间线错位现象，会导致疾病进展相似的患者群体呈现出虚假的差异化生物标志物趋势，损害下游分析的可靠性，并增加疾病亚型分型、预后预测等任务的复杂度。为解决这一挑战，本研究提出一种具备亚型感知能力的时间线配准方法，该方法通过数据投影与离散优化技术校正时间线错位问题。通过模拟实验与真实数据分析，本研究证实所提方法可有效将失真的观测记录与真实疾病进展模式进行对齐，提升亚型分型的清晰度，并优化下游临床分析的任务性能。本文的补充材料可在线获取，其中包含可用于复现本研究成果的标准化材料说明。