Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature

PURPOSE: Supplementing investigator‐specified variables with large numbers of empirically identified features that collectively serve as ‘proxies’ for unspecified or unmeasured factors can often improve confounding control in studies utilizing administrative healthcare databases. Consequently, there...

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Detalles Bibliográficos
Autores principales: Wyss, Richard, Yanover, Chen, El‐Hay, Tal, Bennett, Dimitri, Platt, Robert W., Zullo, Andrew R., Sari, Grammati, Wen, Xuerong, Ye, Yizhou, Yuan, Hongbo, Gokhale, Mugdha, Patorno, Elisabetta, Lin, Kueiyu Joshua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9541861/
https://www.ncbi.nlm.nih.gov/pubmed/35729705
http://dx.doi.org/10.1002/pds.5500

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9541861/
https://www.ncbi.nlm.nih.gov/pubmed/35729705
http://dx.doi.org/10.1002/pds.5500

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