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
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
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, 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