Self-Service Data Science for Adverse Event Prediction in Electronic Healthcare Records.

Healthcare is a data intensive industry in which data mining has a great potential for improving the wellbeing of patients. However, a multitude of barriers impedes the application of machine learning. This work focuses on medical adverse event prediction by domain experts. In this research we present AutoCrisp as a self-service data science prototype for multivariate sequential classification on electronic healthcare records to facilitate self-service data science by domain experts, without requiring any sophisticated data mining knowledge. We performed an empirical case study with the objective to predict bleedings with the use of AutoCrisp. Our results show that multivariate sequential classification for medical adverse event prediction can indeed be made accessible to healthcare professionals by providing appropriate tooling support.