Towards intelligent decision making for risk screening

Predicting the best next test for medical diagnosis is crucial as it can speed up diagnosis and reduce medical expenses. This determination should be made by fully utilizing the available information in a personalized manner for each patient. In this paper, we propose a method that uses synthesis to infer the best learning cohort for the patient under consideration. The constrained sample space is then used to select the best next test by maximizing the expected information gain.