Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-reported outcomes (PROs) using activity tracker data in a cohort of patients with stable ischemic heart disease (SIHD). A population of 182 patients with SIHD were monitored over a period of 12 weeks. Each subject received a Fitbit Charge 2 device to record daily activity data, and each subject completed eight Patient-Reported Outcomes Measurement Information Systems short form at the end of each week as a self-assessment of their health status. Two models were built to classify PRO scores using activity tracker data. The first model treated each week independently, whereas the second used a hidden Markov model (HMM) to take advantage of correlations between successive weeks. Retrospective analysis compared the classification accuracy of the two models and the importance of each feature. In the independent model, a random forest classifier achieved a mean area under curve (AUC) of 0.76 for classifying the physical function PRO. The HMM model achieved significantly better AUCs for all PROs (p < 0.05) other than Fatigue and Sleep Disturbance, with a highest mean AUC of 0.79 for the physical function-short form 10a. Our study demonstrates the ability of activity tracker data to classify health status over time. These results suggest that patient outcomes can be monitored in real time using activity trackers.

中文翻译：

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使用活动跟踪器数据对一组心脏病患者的自我报告健康状况进行分类的机器学习方法


使用个人传感器数据构建统计模型可以跟踪一段时间内的健康状况，从而使早期干预成为可能。本研究的目标是使用机器学习算法，利用一组稳定型缺血性心脏病 (SIHD) 患者的活动跟踪器数据，对患者报告的结果 (PRO) 进行分类。对 182 名 SIHD 患者进行了为期 12 周的监测。每个受试者都会收到一个 Fitbit Charge 2 设备来记录日常活动数据，每个受试者在每周结束时完成八份患者报告结果测量信息系统简表，作为对其健康状况的自我评估。构建了两个模型来使用活动跟踪器数据对 PRO 分数进行分类。第一个模型每周独立处理，而第二个模型使用隐马尔可夫模型（HMM）来利用连续几周之间的相关性。回顾性分析比较了两种模型的分类精度以及每个特征的重要性。在独立模型中，随机森林分类器对物理功能 PRO 进行分类的平均曲线下面积 (AUC) 达到 0.76。 HMM 模型对除疲劳和睡眠障碍之外的所有 PRO 均实现了显着更好的 AUC (p < 0.05)，身体功能短形式 10a 的最高平均 AUC 为 0.79。我们的研究证明了活动跟踪器数据能够随着时间的推移对健康状况进行分类。这些结果表明，可以使用活动追踪器实时监控患者的治疗结果。