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
LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records
Jun Wen, Jue Hou, Clara-Lea Bonzel, Yihan Zhao, Victor M. Castro, Vivian S. Gainer, Dana Weisenfeld, Tianrun Cai, Yuk-Lam Ho, Vidul A. Panickan, Lauren Costa, Chuan Hong, J. Michael Gaziano, Katherine P. Liao, Junwei Lu, Kelly Cho, Tianxi Cai
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods
Raphael Poulain, Mirza Farhan Bin Tarek, Rahmatollah Beheshti
A Method to Automate the Discharge Summary Hospital Course for Neurology Patients
Vince C. Hartman, Sanika S. Bapat, Mark G. Weiner, Babak B. Navi, Evan T. Sholle, Thomas R. Campion,
Extracting Diagnosis Pathways from Electronic Health Records Using Deep Reinforcement Learning
Lillian Muyama, Antoine Neuraz, Adrien Coulet
A Cross-institutional Evaluation on Breast Cancer Phenotyping NLP Algorithms on Electronic Health Records
Sicheng Zhou, Nan Wang, Liwei Wang, Ju Sun, Anne Blaes, Hongfang Liu, Rui Zhang
Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records
Eunbyeol Cho, Min Jae Lee, Kyunghoon Hur, Jiyoun Kim, Jinsung Yoon, Edward Choi