Mining Electronic Health Records for Real-World Evidence

Chengxi Zang, Weishen Pan, Fei Wang

Abstract: The rapid accumulation of large-scale Electronic Health Records (EHR) presents considerable opportunities to generate real-world evidence to inform clinical decision-making and accelerate drug development. However, the complexity of EHR has turned them into a formidable testing ground for cutting-edge AI algorithms. Furthermore, a significant gap still exists between algorithm development in the computer science community and clinical translation within the healthcare community. This tutorial aims to bridge this divide by fostering mutual understanding between the two communities by discussing using advanced machine learning and data mining technologies tailored to tackle real-world healthcare challenges, including 1) using EHR and trial emulation for understanding Long Covid and drug repurposing for Alzheimer's disease, and 2) risk prediction and associated fairness, interpretability, generalizability, etc., issues. We will conclude this tutorial by delving into potential opportunities for future research and unveiling the prospects of a career as a health data scientist.