A Markov model for inferring event types on diabetes patients data

Gathering diabetes-related data requires effort from the patient side to log specific events throughout experiments. This is an error-prone task that the patients usually handle by following a prescribed protocol. However, patients often do not follow the protocol, causing missing or imperfect data. This study investigates the possibility of generating Markov models from existing/logged data and using them for imputation. The models are used to infer information related to missing events (types/activities) in data recorded by diabetes patients, allowing for improvements in the quality and continuity of such data. Our results indicate that such an approach can help improve the quality of the data collected.