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Wednesday, November 30, 2022 – 4:00PM to 5:00PM
Matthew M. Engelhard, Assistant Professor of Biostatistics & Bioinformatics with host Andrew Olson, MPP, Associate Director, Policy Strategy and Solutions for Health Data Science, Duke AI Health
The electronic health record is a rich and still largely untapped source of clinical information that can be coupled with machine learning to support and augment clinical decision-making. However, much of this information is present in clinical notes, images, and other unstructured formats rather than in structured fields. In this 1-hour seminar, we'll discuss why unstructured data is so valuable in many prediction tasks, then learn how to incorporate features from multiple modalities in a single, neural network based predictive model. Although our discussion will focus primarily on electronic health record data, the proposed approach can be applied to a wide range of multi-modal data sources.