Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings.

By Andrew D. Revell, Daisy Wang, R. Harrigan, R. Hamers, A. Wensing, F. Dewolf, M. Nelson, A. Geretti, B. Larder. 2010

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Combining Kernel and Model Based Learning for HIV Therapy Selection (2017, methodology): “[2] use tree-based learning to predict a patient’s virological response using evolutionary information, while others [3, 4] predict virological response based on the patient’s treatment history.”
History-alignment models for bias-aware prediction of virological response to HIV combination therapy (2012, background): “…al., 2003; Larder et al., 2007; Deforche et al., 2008; Rosen-Zvi et al., 2008; Bickel et al., 2008; Altmann et al., 2009; Prosperi et al., 2009; Bogojeska et al., 2010; Revell et al., 2010), have emerged for tackling the problem of predicting the virological response to HIV combination therapies.”
Antiretroviral Therapy Optimisation without Genotype Resistance Testing: A Perspective on Treatment History Based Models (2010, background): “[33], that explored either artificial neural networks or RF for the prediction of virological outcomes in absence of GRT information, using data from Europe, North America, Japan and Australia (.”