Patient data representation for outcome prediction of congestive heart failure patients

Abstract: Artificial Intelligence (AI) has its roots in every field in present scenario. Healthcare is one of the sectors where AI is reaching considerable growth in recent years. Tremendous increase in healthcare data availability and considerable growth in big data analytic methods has paved way for success of AI in healthcare and research is being driven towards improvement in quality of service. Healthcare data is stored in the form of Electronic Health Records (EHR) which consists of temporally ordered patient information. There are many challenges with EHR data like heterogeneity, missing values, biases, noise, temporality etc. This master thesis focuses on addressing the problem of visit level irregularity which refers to irregular timing between events (patient’s visits).