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
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records
Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Bowen Jin, May D. Wang, Joyce C. Ho, Carl Yang
EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries
Sunjun Kweon, Jiyoun Kim, Heeyoung Kwak, Dongchul Cha, Hangyul Yoon, Kwanghyun Kim, Jeewon Yang, Seunghyun Won, Edward Choi
TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Records
Hojjat Karami, David Atienza, Anisoara Ionescu
TimEHR: Image-based Time Series Generation for Electronic Health Records
Hojjat Karami, Mary-Anne Hartley, David Atienza, Anisoara Ionescu
Multimodal Interpretable Data-Driven Models for Early Prediction of Antimicrobial Multidrug Resistance Using Multivariate Time-Series
Sergio Martínez-Agüero, Antonio G. Marques, Inmaculada Mora-Jiménez, Joaquín Alvárez-Rodríguez, Cristina Soguero-Ruiz