Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence.

Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients were followed up at the Swiss interprofessional med...

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Bibliographic Details

Published in:	AIDS Care Vol. 33; no. 4; pp. 530 - 537
Main Authors:	Kamal, Susan, Urata, John, Cavassini, Matthias, Liu, Honghu, Kouyos, Roger, Bugnon, Olivier, Wang, Wei, Schneider, Marie-Paule
Format:	research tables/charts Journal Article
Published:	Taylor & Francis Ltd Apr2021
Online Access:	View this record in EBSCOhost