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
When BERT Fails -- The Limits of EHR Classification
Augusto Garcia-Agundez, Carsten Eickhoff
Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records
Basile Dura, Charline Jean, Xavier Tannier, Alice Calliger, Romain Bey, Antoine Neuraz, Rémi Flicoteaux