Abstract: We present a mixture-of-experts approach for HIV therapy selection. The heterogeneity in patient data makes it difficult for one particular model to succeed at providing suitable therapy predictions for all patients. An appropriate means for addressing this heterogeneity is through combining kernel and model-based techniques. These methods capture different kinds of information: kernel-based methods are able to identify clusters of similar patients, and work well when modelling the viral response for these groups. In contrast, model-based methods capture the sequential process of decision making, and are able to find simpler, yet accurate patterns in response for patients outside these groups. We take advantage of this information by proposing a mixture-of-experts model that automatically selects between the methods in order to assign the most appropriate therapy choice to an individual. Overall, we verify that therapy combinations proposed using this approach significantly outperform previous methods.
Learning Objective 1: We demonstrate that optimising for immediate viral load reduction does not control viral loads or mutations in the long term. We train a model-based learner and combine it with the kernel-based approach via mixture-of-experts. We demonstrate that the therapy combinations proposed by our treatment policy outperform previous approaches.
Sonali Parbhoo (Presenter)
University of Basel
Jasmina Bogojeska, IBM Research Zurich
Maurizio Zazzi, University of Siena
Volker Roth, University of Basel
Finale Doshi-Velez, Harvard University