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Drug cocktails formed by mixing multiple drugs at various doses provide more effective cures than single-drug treatments. However, drugs interact in highly nonlinear ways making the determination of the optimal combination a difficult task. The response surface of the drug cocktail has to be estimated through expensive and time-consuming experimentation. Previous research focused on the use of space-exploratory heuristics such as genetic algorithms to guide the search for optimal combinations. While being more efficient than random sampling, these methods require a considerable amount of experiments to converge to good solutions. In this paper, we propose to use an information-theoretic active learning approach under the Bayesian framework of Gaussian processes to adaptively choose what experiments to perform based on current data points. We show that our approach is able to reduce the number of required data points significantly. [ABSTRACT FROM PUBLISHER]

Janusmed is a clinical decision support system, developed by the Stockholm County Council that supports physicians in identifying drug-drug interactions. To determine how Janusmed is used in and affects the clinical practice, an evaluation study is currently being carried out that analyzes multiple data sources through descriptive statistics. The study focuses on how Janusmed affects the behavior of the physicians, in particular, to what extent physicians reconsider their prescription decisions based on warnings from Janusmed. [ABSTRACT FROM AUTHOR]