University of Florida, Jacksonville, Florida, USA.
Nemours Children's Specialty Care, Jacksonville, Florida, USA.
J Am Med Inform Assoc. 2020 Nov 1;27(11):1808-1812. doi: 10.1093/jamia/ocaa159.
Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.
定义患者间的相似性对于临床护理和研究中的精准医学的发展至关重要。从概念上讲，识别相似的患者队列似乎很简单；然而，普遍接受的定义仍然难以捉摸。与此同时，供应商和已发布算法大量涌现，它们都在识别患者相似类别方面提供了不同程度的功能。为了提供清晰度和患者相似性的通用框架，在美国医学信息学协会 2019 年年会召开了一次研讨会。该研讨会邀请了学术界、生物技术行业、FDA 和私营肿瘤学团体的特邀讨论者。研讨会参与者来自广泛的背景，能够围绕 4 个主要的患者相似类别达成共识：（1）特征，（2）结果，（3）暴露，和（4）混合类。这种观点更深入地扩展到这 4 个亚型，并为医学信息学社区提供了一种交流他们在这一重要主题上工作的手段。
