TOWARDS HIGH QUALITY AND INTERPRETABLE HEALTHCARE DATA ANALYTICS

Abstract

In recent years, the increasing availability of Electronic Medical Records (EMR) has brought a vast array of promising opportunities to automate healthcare data analytics. This helps gradually reduce the need for traditional manual data analytics which relies on domain expertise, experience, and costly as well as painstakingly designed experiments. However, the complexity of EMR data and EMR data analytics poses challenges on healthcare analytic performance, diminishing its potential and hence usability in practice. In this thesis, we study four main challenges in EMR data and EMR data analytics, namely irregularity, bias, the lack of reliability and the lack of interpretability, and propose solutions to resolving them. Consequently, we manage to boost the performance and facilitate high quality as well as interpretable healthcare data analytics for providing useful medical insights.