Electronic Health Record
Electronic health records (EHRs) are digital repositories of patient medical information, aiming to improve healthcare efficiency and quality. Current research focuses on enhancing EHR utility through advanced natural language processing (NLP) techniques, including transformer-based models and graph neural networks, to improve data extraction, analysis, and prediction of patient outcomes. These efforts address challenges like data security, interoperability, and the need for efficient clinical decision support systems, ultimately impacting patient care, research, and administrative workflows. The development of robust and reliable methods for processing and analyzing EHR data is a key area of ongoing investigation.
Papers
Facilitating phenotyping from clinical texts: the medkit library
Antoine Neuraz, Ghislain Vaillant, Camila Arias, Olivier Birot, Kim-Tam Huynh, Thibaut Fabacher, Alice Rogier, Nicolas Garcelon, Ivan Lerner, Bastien Rance, Adrien Coulet
Improving Extraction of Clinical Event Contextual Properties from Electronic Health Records: A Comparative Study
Shubham Agarwal, Thomas Searle, Mart Ratas, Anthony Shek, James Teo, Richard Dobson