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

Nathan D. Seligson, Jeremy L. Warner, William S. Dalton, David Martin, Robert S. Miller, Debra Patt, Kenneth L. Kehl, Matvey B. Palchuk, Gil Alterovitz, Laura K. Wiley, Ming Huang, Feichen Shen, Yanshan Wang, Khoa A. Nguyen, Anthony F. Wong, Funda Meric-Bernstam, Elmer V. Bernstam, James L. Chen

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1808-1812
Number of pages5
JournalJournal of the American Medical Informatics Association
Volume27
Issue number11
DOIs
StatePublished - Nov 1 2020

Keywords

  • patient matching
  • patients like me
  • personalized medicine, similar patients
  • precision medicine

ASJC Scopus subject areas

  • Health Informatics

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