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Consequently, there has been a recent focus on the development of data\u2010driven methods for high\u2010dimensional proxy confounder adjustment in pharmacoepidemiologic research. In this paper, we survey current approaches and recent advancements for high\u2010dimensional proxy confounder adjustment in healthcare database studies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We discuss considerations underpinning three areas for high\u2010dimensional proxy confounder adjustment: (1) feature generation\u2014transforming raw data into covariates (or features) to be used for proxy adjustment; (2) covariate prioritization, selection, and adjustment; and (3) diagnostic assessment. We discuss challenges and avenues of future development within each area.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>There is a large literature on methods for high\u2010dimensional confounder prioritization\/selection, but relatively little has been written on best practices for feature generation and diagnostic assessment. Consequently, these areas have particular limitations and challenges.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>There is a growing body of evidence showing that machine\u2010learning algorithms for high\u2010dimensional proxy\u2010confounder adjustment can supplement investigator\u2010specified variables to improve confounding control compared to adjustment based on investigator\u2010specified variables alone. However, more research is needed on best practices for feature generation and diagnostic assessment when applying methods for high\u2010dimensional proxy confounder adjustment in pharmacoepidemiologic studies.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pds.5500","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T03:48:20Z","timestamp":1655869700000},"page":"932-943","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine learning for improving high\u2010dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature"],"prefix":"10.1002","volume":"31","author":[{"given":"Richard","family":"Wyss","sequence":"first","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Yanover","sequence":"additional","affiliation":[{"name":"KI Research Institute  Kfar Malal Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tal","family":"El\u2010Hay","sequence":"additional","affiliation":[{"name":"KI Research Institute  Kfar Malal Israel"},{"name":"IBM Research\u2013Haifa Labs  Haifa Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8387-9342","authenticated-orcid":false,"given":"Dimitri","family":"Bennett","sequence":"additional","affiliation":[{"name":"Global Evidence and Outcomes Takeda Pharmaceutical Company Ltd.  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