DETECT:电子健康记录中疾病轨迹建模的特征提取方法。

Pankhuri Singhal, Lindsay Guare, Colleen Morse, Anastasia Lucas, Marta Byrska-Bishop, Marie A Guerraty, Dokyoon Kim, Marylyn D Ritchie, Anurag Verma
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引用次数: 0

摘要

鉴于电子健康记录(EHR)中的高维度、冗余和噪声,使用纵向电子健康记录(EHR)数据建模具有挑战性。为了改进精准医疗策略并提前识别疾病风险预测因子,评估有意义的患者疾病轨迹至关重要。在本研究中,我们开发了疾病轨迹特征提取算法(DiseasE Trajectory fEature extraCTion,DETECT),用于在高通量时态电子病历数据中进行特征提取和轨迹生成。该算法可以:1)模拟纵向个体级电子病历数据,根据用户指定的规模、复杂性和噪声参数进行模拟;2)使用收敛相对风险框架测试在指定的索引代码和结果代码之间出现的中间代码,以确定它们是否是结果的预测特征。可以指定时间范围,以调查结果发生前特定时间段内出现的预测因子。我们在模拟数据上对我们的方法进行了基准测试,并使用 DETECT 生成了真实世界的疾病轨迹,对象是宾夕法尼亚大学医学院 EHR 中诊断为高血压的 145,575 名严重心脏代谢疾病患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DETECT: Feature extraction method for disease trajectory modeling in electronic health records.

Modeling with longitudinal electronic health record (EHR) data proves challenging given the high dimensionality, redundancy, and noise captured in EHR. In order to improve precision medicine strategies and identify predictors of disease risk in advance, evaluating meaningful patient disease trajectories is essential. In this study, we develop the algorithm DiseasE Trajectory fEature extraCTion (DETECT) for feature extraction and trajectory generation in high-throughput temporal EHR data. This algorithm can 1) simulate longitudinal individual-level EHR data, specified to user parameters of scale, complexity, and noise and 2) use a convergent relative risk framework to test intermediate codes occurring between specified index code(s) and outcome code(s) to determine if they are predictive features of the outcome. Temporal range can be specified to investigate predictors occurring during a specific period of time prior to onset of the outcome. We benchmarked our method on simulated data and generated real-world disease trajectories using DETECT in a cohort of 145,575 individuals diagnosed with hypertension in Penn Medicine EHR for severe cardiometabolic outcomes.

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