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.
A mixture-of-experts approach was proposed in REF to combine the strengths of both kernel-based regression methods (i.e., historyalignment model) and RL (i.e., model-based Bayesian POMDP model) for HIV therapy selection.
Combining Kernel and Model Based Learning for HIV Therapy Selection
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Computer Science Medicine