Enabling scalable clinical interpretation of machine learning (ML)-based phenotypes using real world data

Abstract

Background: Large and deep electronic health record (EHR) datasets have the potential to increase understanding of real-world patient journeys, and to identify subgroups of patients currently grouped with a common disease label but differing in outcomes and medical need. However, working with EHRs is still relatively new and challenging due to the heterogeneous nature of data. Increasing interest in machine learning (ML)-based EHR aggregation is mostly method-driven, i.e., building on available or newly developed methods. These methods, input requirements, and output are frequently difficult to interpret, especially without data science or statistical training. This endangers the ultimate aim of such analyses: generating actionable and clinically meaningful interpretation.