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Intersections of machine learning and epidemiological methods for health services research

The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learn...

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Detalles Bibliográficos
Autor principal: Rose, Sherri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7825941/
https://www.ncbi.nlm.nih.gov/pubmed/32236476
http://dx.doi.org/10.1093/ije/dyaa035
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author Rose, Sherri
author_facet Rose, Sherri
author_sort Rose, Sherri
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description The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research.
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spelling pubmed-78259412021-03-11 Intersections of machine learning and epidemiological methods for health services research Rose, Sherri Int J Epidemiol Opinion The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research. Oxford University Press 2020-04-01 /pmc/articles/PMC7825941/ /pubmed/32236476 http://dx.doi.org/10.1093/ije/dyaa035 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Opinion
Rose, Sherri
Intersections of machine learning and epidemiological methods for health services research
title Intersections of machine learning and epidemiological methods for health services research
title_full Intersections of machine learning and epidemiological methods for health services research
title_fullStr Intersections of machine learning and epidemiological methods for health services research
title_full_unstemmed Intersections of machine learning and epidemiological methods for health services research
title_short Intersections of machine learning and epidemiological methods for health services research
title_sort intersections of machine learning and epidemiological methods for health services research
topic Opinion
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7825941/
https://www.ncbi.nlm.nih.gov/pubmed/32236476
http://dx.doi.org/10.1093/ije/dyaa035
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