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
Shimo Lab at "Discharge Me!": Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sections
Yunzhen He, Hiroaki Yamagiwa, Hidetoshi Shimodaira
EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction Using Large Language Multimodal Models
Chun-Chieh Liao, Wei-Ting Kuo, I-Hsuan Hu, Yen-Chen Shih, Jun-En Ding, Feng Liu, Fang-Ming Hung
Assessing the role of clinical summarization and patient chart review within communications, medical management, and diagnostics
Chanseo Lee, Kimon-Aristotelis Vogt, Sonu Kumar
EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records
Yeonsu Kwon, Jiho Kim, Gyubok Lee, Seongsu Bae, Daeun Kyung, Wonchul Cha, Tom Pollard, Alistair Johnson, Edward Choi