Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial.
Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this a...
| Publicado en: | Ethnicity & Disease Vol. 30; pp. 217 - 229 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
KnowledgeWorks Global, Ltd
2020 Supplement 1
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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=142612539&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142612539 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1049510X 2QJY jtl: Ethnicity & Disease issn: 1049510X maglogo: N pubinfo: dt: 2020 Supplement 1 vid: 30 pid: 81084 pub: KnowledgeWorks Global, Ltd place: Richmond, Virginia artinfo: ui: 142612539 142612539 NLM32269464 142612539 10.18865/ed.30.S1.217 NLM32269464 142612539 ppf: 217 ppct: 12 formats: tig: atl: Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial. aug: au: Basu, Sanjay Faghmous, James H. Doupe, Patrick affil: Research and Analytics, Collective Health, San Francisco, CA sug: subj: Health Status Disparities Human ab: Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this article, we provide a structured tutorial for medical and public health researchers on the application of machine learning methods to conduct precision medicine research designed to reduce health disparities. We review key terms and concepts for understanding machine learning papers, including supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting. Metrics are reviewed for evaluating machine learners and major families of learning approaches, including tree-based learning, deep learning, and ensemble learning. We highlight the advantages and disadvantages of different learning approaches, describe strategies for interpreting "black box" models, and demonstrate the application of common methods in an example dataset with open-source statistical code in R. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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