Stable warfarin dose prediction in sub‐Saharan African patients: A machine‐learning approach and external validation of a clinical dose-initiation algorithm

• Egile Nagusiak: Innocent G. Asiimwe (Egilea), Marc Blockman (Egilea), Karen Cohen (Egilea), Clint Cupido (Egilea), Claire Hutchinson (Egilea), Barry Jacobson (Egilea), Mohammed Lamorde (Egilea), Jennie Morgan (Egilea), Johannes P. Mouton (Egilea), Doreen Nakagaayi (Egilea), Emmy Okello (Egilea), Elise Schapkaitz (Egilea), Christine Sekaggya‐Wiltshire (Egilea), Jerome R. Semakula (Egilea), Catriona Waitt (Egilea), Eunice J. Zhang (Egilea), Andrea L. Jorgensen (Egilea), Munir Pirmohamed (Egilea)
• Formatua: Liburua
• Argitaratua: Wiley, 2022-01-01T00:00:00Z.
• Gaiak: article
• Sarrera elektronikoa: Connect to this object online.

Abstract Warfarin remains the most widely prescribed oral anticoagulant in sub‐Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21 machine‐learning techniques in predicting stable warfarin dos...