Machine Learning, Health Disparities, and Causal Reasoning.
Rajkomar and colleagues warn us that the introduction of machine-learned predictive algorithms into medicine might inadvertently reinforce or create inequitable treatment of protected groups, for which the computer science community has adopted the terminology of "fairness." The editorialists discus...
| Publicado en: | Annals of Internal Medicine Vol. 169; no. 12; pp. 883 - 885 |
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| Autores principales: | , , |
| Formato: | commentary Journal Article |
| Publicado: |
American College of Physicians
12/18/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133645707&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133645707 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00034819 AIM jtl: Annals of Internal Medicine issn: 00034819 maglogo: N pubinfo: dt: 12/18/2018 vid: 169 iid: 12 pid: 1652 pub: American College of Physicians place: Philadelphia, Pennsylvania artinfo: ui: 133645707 133645707 NLM30508423 133645707 10.7326/M18-3297 NLM30508423 133645707 ppf: 883 ppct: 2 formats: tig: atl: Machine Learning, Health Disparities, and Causal Reasoning. aug: au: Goodman, Steven N. Goel, Sharad Cullen, Mark R. affil: Stanford University School of Medicine, Stanford, California sug: subj: Problem Solving ab: Rajkomar and colleagues warn us that the introduction of machine-learned predictive algorithms into medicine might inadvertently reinforce or create inequitable treatment of protected groups, for which the computer science community has adopted the terminology of "fairness." The editorialists discuss the authors' proposals and conclude that no formulaic solution will be sufficient to achieve fairness. pubtype: Academic Journal doctype: commentary Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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