Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity. (4th September 2020)

• Record Type: Journal Article
• Authors: Seligson, Nathan D; Warner, Jeremy L; Dalton, William S; Martin, David; Miller, Robert S; Patt, Debra; Kehl, Kenneth L; Palchuk, Matvey B; Alterovitz, Gil; Wiley, Laura K; Huang, Ming; Shen, Feichen; Wang, Yanshan; Nguyen, Khoa A; Wong, Anthony F; Meric-Bernstam, Funda; Bernstam, Elmer V; Chen, James L
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 11(2020)
• Page Start: 1808
• Page End: 1812
• Publication Date: 2020-09-04
• Subjects: patient matching -- precision medicine -- patients like me -- personalized medicine, similar patients; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa159
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: 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.