Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity

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 expl...

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Veröffentlicht in:Journal of the American Medical Informatics Association : JAMIA 2020-11, Vol.27 (11), p.1808-1812
Hauptverfasser: 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
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container_end_page 1812
container_issue 11
container_start_page 1808
container_title Journal of the American Medical Informatics Association : JAMIA
container_volume 27
creator 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
description 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.
doi_str_mv 10.1093/jamia/ocaa159
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subjects Female
Humans
Male
Medical Informatics
Precision Medicine
Terminology as Topic
title Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity
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