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
CRIS: Collaborative Refinement Integrated with Segmentation for Polyp Segmentation
Ankush Gajanan Arudkar, Bernard J. E. Evans
Leveraging Open-Source Large Language Models for encoding Social Determinants of Health using an Intelligent Router
Akul Goel, Surya Narayanan Hari, Belinda Waltman, Matt Thomson
EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records
Adibvafa Fallahpour, Mahshid Alinoori, Wenqian Ye, Xu Cao, Arash Afkanpour, Amrit Krishnan
EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records
Jaehee Ryu, Seonhee Cho, Gyubok Lee, Edward Choi
WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV
Hendrik Damm, Tabea M. G. Pakull, Bahadır Eryılmaz, Helmut Becker, Ahmad Idrissi-Yaghir, Henning Schäfer, Sergej Schultenkämper, Christoph M. Friedrich
LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs
Yongrae Jo, Seongyun Lee, Minju Seo, Sung Ju Hwang, Moontae Lee