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We evaluated the potential of real\u2010time EAM (transferred via cellular signal) and standard EAM (downloaded via USB cable) in rural Uganda to inform individually differentiated viral load testing strategies by applying machine\u00a0learning approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We evaluated an observational cohort of persons living with HIV and treated with antiretroviral therapy (ART) who were monitored longitudinally with standard EAM from 2005 to 2011 and real\u2010time EAM from 2011 to 2015. Super learner, an ensemble machine\u00a0learning method, was used to develop a tool for targeting viral load testing to detect viraemia (&gt;1000\u00a0copies\/ml) based on clinical (CD4 count, ART regimen), viral load and demographic data, together with EAM\u2010based adherence. Using sample\u2010splitting (cross\u2010validation), we evaluated area under the receiver operating characteristic curve (cvAUC), potential for EAM data to selectively defer viral load tests while minimizing delays in viraemia detection, and performance compared to WHO\u2010recommended testing schedules.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In total, 443 persons (1801 person\u2010years) and 485 persons (930 person\u2010years) contributed to standard and real\u2010time EAM analyses respectively. In the 2011 to 2015 dataset, addition of real\u2010time EAM (cvAUC: 0.88; 95% CI: 0.83, 0.93) significantly improved prediction compared to clinical\/demographic data alone (cvAUC: 0.78; 95% CI: 0.72, 0.86; <jats:italic>p<\/jats:italic>\u00a0=\u00a00.03). In the 2005 to 2011 dataset, addition of standard EAM (cvAUC: 0.77; 95% CI: 0.72, 0.81) did not significantly improve prediction compared to clinical\/demographic data alone (cvAUC: 0.70; 95% CI: 0.64, 0.76; <jats:italic>p<\/jats:italic>\u00a0=\u00a00.08). A hypothetical testing strategy using real\u2010time EAM to guide deferral of viral load tests would have reduced the number of tests by 32% while detecting 87% of viraemia cases without delay. By comparison, the WHO\u2010recommended testing schedule would have reduced the number of tests by 69%, but resulted in delayed detection of viraemia a mean of 74\u00a0days for 84% of individuals with viraemia. Similar rules derived from standard EAM also resulted in potential testing frequency reductions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our machine learning approach demonstrates potential for combining EAM data with other clinical measures to develop a selective testing rule that reduces number of viral load tests ordered, while still identifying those at highest risk for viraemia.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jia2.25467","type":"journal-article","created":{"date-parts":[[2020,3,23]],"date-time":"2020-03-23T08:42:21Z","timestamp":1584952941000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Super learner analysis of real\u2010time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non\u2010differentiated care approaches for persons living with HIV in rural Uganda"],"prefix":"10.1002","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4826-3611","authenticated-orcid":false,"given":"Alejandra E","family":"Benitez","sequence":"first","affiliation":[{"name":"Division of Biostatistics School of Public Health University of California Berkeley Berkeley CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Musinguzi","sequence":"additional","affiliation":[{"name":"Global Health Collaborative Mbarara University of Science and Technology Mbarara Uganda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David R","family":"Bangsberg","sequence":"additional","affiliation":[{"name":"Oregon Health &amp; Science University\u2010Portland State University School of Public Health Portland OR USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mwebesa B","family":"Bwana","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Mbarara University of Science &amp; Technology Mbarara Uganda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Conrad","family":"Muzoora","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Mbarara University of Science &amp; Technology Mbarara Uganda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter W","family":"Hunt","sequence":"additional","affiliation":[{"name":"Division of Experimental Medicine University of California San Francisco San Francisco CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey N","family":"Martin","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics University of California San Francisco San Francisco CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica E","family":"Haberer","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital Center for Global Health Boston MA USA"},{"name":"Harvard Medical School Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maya L","family":"Petersen","sequence":"additional","affiliation":[{"name":"Division of Biostatistics School of Public Health University of California Berkeley Berkeley CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,3,23]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"UNAIDS.UNAIDS Data 2017. 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