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 |
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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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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.</description><identifier>ISSN: 1527-974X</identifier><identifier>ISSN: 1067-5027</identifier><identifier>EISSN: 1527-974X</identifier><identifier>DOI: 10.1093/jamia/ocaa159</identifier><identifier>PMID: 32885823</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Female ; Humans ; Male ; Medical Informatics ; Precision Medicine ; Terminology as Topic</subject><ispartof>Journal of the American Medical Informatics Association : JAMIA, 2020-11, Vol.27 (11), p.1808-1812</ispartof><rights>The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. 2020</rights><rights>The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c420t-60246062cef3e0c0973b80a00d5c2905930d6802070f6480b290da1b41d7850c3</citedby><cites>FETCH-LOGICAL-c420t-60246062cef3e0c0973b80a00d5c2905930d6802070f6480b290da1b41d7850c3</cites><orcidid>0000-0002-2851-7242 ; 0000-0003-4433-7839 ; 0000-0001-6816-6072 ; 0000-0001-7367-3626 ; 0000-0001-6681-9754 ; 0000-0003-0869-6316 ; 0000-0001-5185-5170 ; 0000-0002-9670-3792 ; 0000-0002-7737-8752 ; 0000-0003-4517-0380</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671612/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671612/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,1578,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32885823$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Seligson, Nathan D</creatorcontrib><creatorcontrib>Warner, Jeremy L</creatorcontrib><creatorcontrib>Dalton, William S</creatorcontrib><creatorcontrib>Martin, David</creatorcontrib><creatorcontrib>Miller, Robert S</creatorcontrib><creatorcontrib>Patt, Debra</creatorcontrib><creatorcontrib>Kehl, Kenneth L</creatorcontrib><creatorcontrib>Palchuk, Matvey B</creatorcontrib><creatorcontrib>Alterovitz, Gil</creatorcontrib><creatorcontrib>Wiley, Laura K</creatorcontrib><creatorcontrib>Huang, Ming</creatorcontrib><creatorcontrib>Shen, Feichen</creatorcontrib><creatorcontrib>Wang, Yanshan</creatorcontrib><creatorcontrib>Nguyen, Khoa A</creatorcontrib><creatorcontrib>Wong, Anthony F</creatorcontrib><creatorcontrib>Meric-Bernstam, Funda</creatorcontrib><creatorcontrib>Bernstam, Elmer V</creatorcontrib><creatorcontrib>Chen, James L</creatorcontrib><title>Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity</title><title>Journal of the American Medical Informatics Association : JAMIA</title><addtitle>J Am Med Inform Assoc</addtitle><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.</description><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Medical Informatics</subject><subject>Precision Medicine</subject><subject>Terminology as Topic</subject><issn>1527-974X</issn><issn>1067-5027</issn><issn>1527-974X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNqFkUtLxDAUhYMojq-lW8nSTZ2bR9PWhTAMPgYUQRTchUyazmRsm5q0yvx7q446guDqHu49fPfAQeiQwAmBjA0XqrJq6LRSJM420A6JaRJlCX_cXNMDtBvCAoAIyuJtNGA0TeOUsh3U3BntqsrUuWqtqwMunMdNr03d4mArWypv2yXWpQrBhFPsTejKNmBX4HZu8OhmMsIUSIZfnX8Kc9dgV-PcFLa29ewP0j7aKlQZzMFq7qGHi_P78VV0fXs5GY-uI80ptJEAygUIqk3BDGjIEjZNQQHksaYZxBmDXKRAIYFC8BSm_TJXZMpJnqQxaLaHzj65TTetTK77FF6VsvG2Un4pnbLy96W2czlzLzIRCRGE9oDjFcC7586EVlY2aFOWqjauC5JyDlxwmrHeGn1atXcheFN8vyEg31uSHy3JVUu9_2g927f7q5af365r_mG9AfFBnsw</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Seligson, Nathan D</creator><creator>Warner, Jeremy L</creator><creator>Dalton, William S</creator><creator>Martin, David</creator><creator>Miller, Robert S</creator><creator>Patt, Debra</creator><creator>Kehl, Kenneth L</creator><creator>Palchuk, Matvey B</creator><creator>Alterovitz, Gil</creator><creator>Wiley, Laura K</creator><creator>Huang, Ming</creator><creator>Shen, Feichen</creator><creator>Wang, Yanshan</creator><creator>Nguyen, Khoa A</creator><creator>Wong, Anthony F</creator><creator>Meric-Bernstam, Funda</creator><creator>Bernstam, Elmer V</creator><creator>Chen, James L</creator><general>Oxford University Press</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-2851-7242</orcidid><orcidid>https://orcid.org/0000-0003-4433-7839</orcidid><orcidid>https://orcid.org/0000-0001-6816-6072</orcidid><orcidid>https://orcid.org/0000-0001-7367-3626</orcidid><orcidid>https://orcid.org/0000-0001-6681-9754</orcidid><orcidid>https://orcid.org/0000-0003-0869-6316</orcidid><orcidid>https://orcid.org/0000-0001-5185-5170</orcidid><orcidid>https://orcid.org/0000-0002-9670-3792</orcidid><orcidid>https://orcid.org/0000-0002-7737-8752</orcidid><orcidid>https://orcid.org/0000-0003-4517-0380</orcidid></search><sort><creationdate>20201101</creationdate><title>Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity</title><author>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</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-60246062cef3e0c0973b80a00d5c2905930d6802070f6480b290da1b41d7850c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Female</topic><topic>Humans</topic><topic>Male</topic><topic>Medical Informatics</topic><topic>Precision Medicine</topic><topic>Terminology as Topic</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Seligson, Nathan D</creatorcontrib><creatorcontrib>Warner, Jeremy L</creatorcontrib><creatorcontrib>Dalton, William S</creatorcontrib><creatorcontrib>Martin, David</creatorcontrib><creatorcontrib>Miller, Robert S</creatorcontrib><creatorcontrib>Patt, Debra</creatorcontrib><creatorcontrib>Kehl, Kenneth L</creatorcontrib><creatorcontrib>Palchuk, Matvey B</creatorcontrib><creatorcontrib>Alterovitz, Gil</creatorcontrib><creatorcontrib>Wiley, Laura K</creatorcontrib><creatorcontrib>Huang, Ming</creatorcontrib><creatorcontrib>Shen, Feichen</creatorcontrib><creatorcontrib>Wang, Yanshan</creatorcontrib><creatorcontrib>Nguyen, Khoa A</creatorcontrib><creatorcontrib>Wong, Anthony F</creatorcontrib><creatorcontrib>Meric-Bernstam, Funda</creatorcontrib><creatorcontrib>Bernstam, Elmer V</creatorcontrib><creatorcontrib>Chen, James L</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of the American Medical Informatics Association : JAMIA</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Seligson, Nathan D</au><au>Warner, Jeremy L</au><au>Dalton, William S</au><au>Martin, David</au><au>Miller, Robert S</au><au>Patt, Debra</au><au>Kehl, Kenneth L</au><au>Palchuk, Matvey B</au><au>Alterovitz, Gil</au><au>Wiley, Laura K</au><au>Huang, Ming</au><au>Shen, Feichen</au><au>Wang, Yanshan</au><au>Nguyen, Khoa A</au><au>Wong, Anthony F</au><au>Meric-Bernstam, Funda</au><au>Bernstam, Elmer V</au><au>Chen, James L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity</atitle><jtitle>Journal of the American Medical Informatics Association : JAMIA</jtitle><addtitle>J Am Med Inform Assoc</addtitle><date>2020-11-01</date><risdate>2020</risdate><volume>27</volume><issue>11</issue><spage>1808</spage><epage>1812</epage><pages>1808-1812</pages><issn>1527-974X</issn><issn>1067-5027</issn><eissn>1527-974X</eissn><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.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>32885823</pmid><doi>10.1093/jamia/ocaa159</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-2851-7242</orcidid><orcidid>https://orcid.org/0000-0003-4433-7839</orcidid><orcidid>https://orcid.org/0000-0001-6816-6072</orcidid><orcidid>https://orcid.org/0000-0001-7367-3626</orcidid><orcidid>https://orcid.org/0000-0001-6681-9754</orcidid><orcidid>https://orcid.org/0000-0003-0869-6316</orcidid><orcidid>https://orcid.org/0000-0001-5185-5170</orcidid><orcidid>https://orcid.org/0000-0002-9670-3792</orcidid><orcidid>https://orcid.org/0000-0002-7737-8752</orcidid><orcidid>https://orcid.org/0000-0003-4517-0380</orcidid><oa>free_for_read</oa></addata></record> |
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source | Oxford University Press Journals All Titles (1996-Current); MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central |
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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