AI Health HDS Learning Experience: KESER: Clinical Knowledge Extraction via Sparse Embedding Regression with EHR data

Traditional data mining of EHR data often requires the use of patient-level data, which hinders the ability to share data across institutions. KESER is a knowledge extraction pipeline via sparse embedding regression, which efficiently summarizes patient-level longitudinal EHR data into hospital-specific embedding data and enables the extraction of clinical knowledge based only on summary-level data. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data. Learn more about KESER at https://www.nature.com/articles/s41746-021-00519-z This seminar is part of the Health Data Science (HDS) Program learning experiences, which focus on applications of machine learning and use cases in health. Register here: https://duke.zoom.us/webinar/register/WN_cjcCbVyLSjC1ViQtKNyD0g