{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T23:52:33Z","timestamp":1792453953767,"version":"4.5.0"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CPT Pharmacom &amp; Syst Pharma"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Warfarin remains the most widely prescribed oral anticoagulant in sub\u2010Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21\u00a0machine\u2010learning techniques in predicting stable warfarin dose in sub\u2010Saharan Black\u2010African patients and (b) externally validated a previously developed Warfarin Anticoagulation in Patients in Sub\u2010Saharan Africa (War\u2010PATH) clinical dose\u2013initiation algorithm. The development cohort included 364 patients recruited from eight outpatient clinics and hospital departments in Uganda and South Africa (June 2018\u2013July 2019). Validation was conducted using an external validation cohort (270 patients recruited from August 2019 to March 2020 in 12 outpatient clinics and hospital departments). Based on the mean absolute error (MAE; mean of absolute differences between the actual and predicted doses), random forest regression (12.07 mg\/week; 95% confidence interval [CI], 10.39\u201313.76) was the best performing machine\u2010learning technique in the external validation cohort, whereas the worst performing technique was model trees (17.59 mg\/week; 95% CI, 15.75\u201319.43). By comparison, the simple, commonly used regression technique (ordinary least squares) performed similarly to more complex supervised machine\u2010learning techniques and achieved an MAE of 13.01 mg\/week (95% CI, 11.45\u201314.58). In summary, we have demonstrated that simpler regression techniques perform similarly to more complex supervised machine\u2010learning techniques. We have also externally validated our previously developed clinical dose\u2013initiation algorithm, which is being prospectively tested for clinical utility.<\/jats:p>","DOI":"10.1002\/psp4.12740","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T01:46:10Z","timestamp":1639100770000},"page":"20-29","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Stable warfarin dose prediction in sub\u2010Saharan African patients: A machine\u2010learning approach and external validation of a clinical dose\u2013initiation algorithm"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1196-1822","authenticated-orcid":false,"given":"Innocent G.","family":"Asiimwe","sequence":"first","affiliation":[{"name":"Department of Pharmacology and Therapeutics The Wolfson Centre for Personalized Medicine Medical Research Council Centre for Drug Safety Science Institute of 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