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

Author:

Kamal Susan1234,Urata John34,Cavassini Matthias5,Liu Honghu678,Kouyos Roger9,Bugnon Olivier12,Wang Wei410,Schneider Marie-Paule12

Affiliation:

1. Community pharmacy, School of pharmaceutical sciences, University of Geneva, University of Lausanne, Lausanne, Switzerland

2. Community pharmacy, Department of ambulatory care & community medicine, University of Lausanne, Lausanne, Switzerland

3. Department of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA

4. University of California Institute for Prediction Technology, University of California, Los Angeles, CA, USA

5. Infectious Disease Service, Lausanne university hospital, University of Lausanne, Lausanne, Switzerland

6. Division of Public Health and Community Dentistry, School of Dentistry, University of California, Los Angeles, CA, USA

7. Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA

8. Department of Medicine, Division of General Internal Medicine and Health Services Research, University of California, Los Angeles, CA, USA

9. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland

10. Department of Computer Science, University of California, Los Angeles, CA, USA

Funder

Swiss National Science Foundation

Swiss HIV Cohort Study

Publisher

Informa UK Limited

Subject

Public Health, Environmental and Occupational Health,Social Psychology,Health (social science)

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