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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.<\/jats:p>","DOI":"10.1093\/jamia\/ocaa159","type":"journal-article","created":{"date-parts":[[2020,7,24]],"date-time":"2020-07-24T19:13:11Z","timestamp":1595617991000},"page":"1808-1812","source":"Crossref","is-referenced-by-count":24,"title":["Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9670-3792","authenticated-orcid":false,"given":"Nathan D","family":"Seligson","sequence":"first","affiliation":[{"name":"University of Florida, Jacksonville, Florida, USA"},{"name":"Nemours Children's Specialty Care, Jacksonville, Florida, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2851-7242","authenticated-orcid":false,"given":"Jeremy L","family":"Warner","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, Tennessee, USA"}]},{"given":"William S","family":"Dalton","sequence":"additional","affiliation":[{"name":"M2Gen, Tampa, Florida, USA"},{"name":"H. 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