利用时间序列电子病历数据对非缺血性心肌病进行测试前预测。

Kary Ishwaran, Bryan Q Abadie, Po-Hao Chen, Michael Bolen, Tara Karamlou, Richard Grimm, W H Wilson Tang, Christopher Nguyen, Deborah Kwon, David Chen
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引用次数: 0

摘要

临床影像学检查是诊断非缺血性心肌病(NICM)的重要诊断方法。然而,要准确解读影像学检查结果,读者往往需要回顾患者病史,这是一项耗时且繁琐的工作。我们建议使用时间序列分析法,利用纵向电子健康记录(EHR)作为 EHR 记录的伪摘要来预测最有可能发生的 NICM。时间序列格式的电子病历数据可以提供对准确预测疾病非常重要的时间信息。具体来说,我们利用 ICD-10 编码和各种递归神经网络架构进行预测建模。我们在一大批接受心脏磁共振成像(CMR)检查的 NICM 患者和一小批接受超声心动图检查的患者身上训练了我们的模型。在心脏磁共振成像(CMR)的所有模型中,所提出技术的微曲线下面积(0.8357)、F1 分数(0.5708)和 3 倍精度(0.8078)均表现良好,但在经胸超声心动图(TTE)中表现一般,分别为 0.6938、0.4399 和 0.5864。我们的研究表明,我们的模型有可能提供准确的检查前鉴别诊断,从而减轻医生的文书工作负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pre-test Prediction of Non-ischemic Cardiomyopathies using Time-Series EHR Data.

Clinical imaging is an important diagnostic test to diagnose non-ischemic cardiomyopathies (NICM). However, accurate interpretation of imaging studies often requires readers to review patient histories, a time consuming and tedious task. We propose to use time-series analysis to predict the most likely NICMs using longitudinal electronic health records (EHR) as a pseudo-summary of EHR records. Time-series formatted EHR data can provide temporality information important towards accurate prediction of disease. Specifically, we leverage ICD-10 codes and various recurrent neural network architectures for predictive modeling. We trained our models on a large cohort of NICM patients who underwent cardiac magnetic resonance imaging (CMR) and a smaller cohort undergoing echocardiogram. The performance of the proposed technique achieved good micro-area under the curve (0.8357), F1 score (0.5708) and precision at 3 (0.8078) across all models for cardiac magnetic resonance imaging (CMR) but only moderate performance for transthoracic echocardiogram (TTE) of 0.6938, 0.4399 and 0.5864 respectively. We show that our model has the potential to provide accurate pre-test differential diagnosis, thereby potentially reducing clerical burden on physicians.

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