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Item Discover the Expert Context Adaptive Expert Selection for Medical Diagnosis(2017-08-14T10:07:00Z) Nelluru Vishnu, Teja; Vedavathi, M S; Akashay, PAn expert selection system that learns online the best expert to assign to each patient depending on the context of the patient is proposed. In general, the context can include an enormous number and variety of information related to the patient's health condition, age, gender, previous drug doses, and so forth, but the most relevant information is embedded in only a few contexts. If these most relevant contexts were known in advance, learning would be relatively simple but they are not. Moreover, the relevant contexts may be different for different health conditions. To address these challenges, a new class of algorithms is developed that are aimed at discovering the most relevant contexts and the best clinic and expert to use to make a diagnosis given a patient's contexts. It is proved that as the number of patients grows, the proposed context adaptive algorithm will discover the optimal expert to select for patients with a specific context. Moreover, the algorithm also provides confidence bounds on the diagnostic accuracy of the expert it selects, which can be considered by the primary care physician before making the final decision. While the algorithm is general and can be applied in numerous medical scenarios, its functionality and performance is illustrated by applying it to a real-world breast cancer diagnosis data set. Finally, while the application considered here is medical diagnosis, the proposed algorithm can be applied in other environments where expertise needs to be discovered.