IEEE J Biomed Health Inform. 2015 May;19(3):1053-60. doi: 10.1109/JBHI.2015.2425365. Epub 2015 Apr 22.
Patient similarity is an important analytic operation in healthcare applications. At the core, patient similarity takes an index patient as the input and retrieves a ranked list of similar patients that are relevant in a specific clinical context. It takes patient information such as their electronic health records as input and computes the distance between a pair of patients based on those information. To construct a clinically valid similarity measure, physician input often needs to be incorporated. However, obtaining physicians' input is difficult and expensive. As a result, typically only limited physician feedbacks can be obtained on a small portion of patients. How to leverage all unlabeled patient data and limited supervision information from physicians to construct a clinically meaningful distance metric? In this paper, we present a patient similarity framework (PSF) that unifies and significantly extends existing supervised patient similarity metric learning methods. PSF is a general framework that can learn an appropriate distance metric through supervised and unsupervised information. Within PSF framework, we propose a novel patient similarity algorithm that uses local spline regression to capture the unsupervised information. To speedup the incorporation of physician feedback or newly available clinical information, we introduce a general online update algorithm for an existing PSF distance metric.
患者相似度是医疗保健应用中的一项重要分析操作。从本质上讲，患者相似度以索引患者作为输入，并在特定的临床环境中检索相关的相似患者的排名列表。它以患者信息（如电子健康记录）作为输入，并根据这些信息计算一对患者之间的距离。为了构建临床有效的相似度度量标准，通常需要纳入医生的输入。然而，获取医生的输入既困难又昂贵。因此，通常只能在一小部分患者中获得有限的医生反馈。如何利用所有未标记的患者数据和医生的有限监督信息来构建有临床意义的距离度量标准？在本文中，我们提出了一个患者相似度框架（PSF），它统一并显著扩展了现有的有监督患者相似度度量学习方法。PSF 是一个通用框架，可以通过有监督和无监督信息学习到合适的距离度量标准。在 PSF 框架内，我们提出了一种新颖的患者相似度算法，该算法使用局部样条回归来捕获无监督信息。为了加快医生反馈或新出现的临床信息的融入，我们为现有的 PSF 距离度量标准引入了一种通用的在线更新算法。
