Lehman Li-wei H, Nemati Shamim, Adams Ryan P, Mark Roger G
Massachusetts Institute of Technology, 45 Carleton Street, Cambridge, MA 02142, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5939-42. doi: 10.1109/EMBC.2012.6347346.
Modern clinical databases include time series of vital signs, which are often recorded continuously during a hospital stay. Over several days, these recordings may yield many thousands of samples. In this work, we explore the feasibility of characterizing the "state of health" of a patient using the physiological dynamics inferred from these time series. The ultimate objective is to assist clinicians in allocating resources to high-risk patients. We hypothesize that "similar" patients exhibit similar dynamics and the properties and duration of these states are indicative of health and disease. We use Bayesian nonparametric machine learning methods to discover shared dynamics in patients' blood pressure (BP) time series. Each such "dynamic" captures a distinct pattern of evolution of BP and is possibly recurrent within the same time series and shared across multiple patients. Next, we examine the utility of this low-dimensional representation of BP time series for predicting mortality in patients. Our results are based on an intensive care unit (ICU) cohort of 480 patients (with 16% mortality) and indicate that the dynamics of time series of vital signs can be an independent useful predictor of outcome in ICU.
现代临床数据库包含生命体征的时间序列，这些数据通常在患者住院期间持续记录。在几天时间里，这些记录可能会产生数千个样本。在这项研究中，我们探讨了利用从这些时间序列中推断出的生理动态特征来描述患者“健康状态”的可行性。最终目标是帮助临床医生为高危患者分配资源。我们假设“相似”的患者表现出相似的动态特征，并且这些状态的属性和持续时间能够反映健康和疾病状况。我们使用贝叶斯非参数机器学习方法来发现患者血压（BP）时间序列中的共享动态特征。每一个这样的“动态特征”都捕捉到了血压演变的独特模式，并且可能在同一时间序列中反复出现，并在多个患者之间共享。接下来，我们检验这种血压时间序列的低维表示在预测患者死亡率方面的效用。我们的研究结果基于一个包含480名患者的重症监护病房（ICU）队列（死亡率为16%），结果表明生命体征时间序列的动态特征可以作为ICU患者预后的一个独立且有用的预测指标。
