Learning to Ask Medical Questions using Reinforcement Learning

Feature selection is an important topic in traditional machine learning [Li et al., 2018], which motivated a large number of widely adopted works, e.g., Lasso [Tibshirani, 1996]. In various cases, the process of obtaining input measurements requires considerable effort (e.g., time, money, technology). For example, in medical datasets input features may correspond to lab tests, medical imaging results, or even answers to questionnaires, which are expensive and slow to produce. Allowing oneself to to be able to accurately predict a response variable from a small set of input features is thus a desirable goal, which can be manifested in saving time, money, and sometimes even human lives. As a running example, consider the case of a patient complaining to a family doctor about not feeling well. The doctor then asks the patients several questions about his current condition and medical background, and may also ask the patient to do some lab tests. Implicitly, the doctor is aiming at quickly collecting relevant details on the patient that will allow her to have a clear understanding of the patients' medical status, and consequently decide on an appropriate action (e.g., medication prescription, admit for hospitalization etc.).