Clinical Note
Clinical notes, the cornerstone of patient care documentation, are increasingly the focus of research aimed at automating their creation and analysis to alleviate physician workload and improve care quality. Current research utilizes large language models (LLMs), often incorporating techniques like retrieval-augmented generation and fine-tuning strategies, to generate notes from various data sources, including doctor-patient conversations and existing records, and to extract key information for tasks such as phenotyping and diagnostic reasoning. This work is significant because it addresses the substantial administrative burden on healthcare professionals, potentially leading to improved efficiency, reduced burnout, and enhanced accuracy in diagnosis and treatment planning.
Papers
A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes
Terri Elizabeth Workman, Joel Kupersmith, Phillip Ma, Christopher Spevak, Friedhelm Sandbrink, Yan Cheng Qing Zeng-Treitler
Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling
Jingyu Xu, Yifeng Jiang, Bin Yuan, Shulin Li, Tianbo Song
Exploring the Consistency, Quality and Challenges in Manual and Automated Coding of Free-text Diagnoses from Hospital Outpatient Letters
Warren Del-Pinto, George Demetriou, Meghna Jani, Rikesh Patel, Leanne Gray, Alex Bulcock, Niels Peek, Andrew S. Kanter, William G Dixon, Goran Nenadic
Extracting periodontitis diagnosis in clinical notes with RoBERTa and regular expression
Yao-Shun Chuang, Chun-Teh Lee, Ryan Brandon, Trung Duong Tran, Oluwabunmi Tokede, Muhammad F. Walji, Xiaoqian Jiang
Parameter-Efficient Methods for Metastases Detection from Clinical Notes
Maede Ashofteh Barabadi, Xiaodan Zhu, Wai Yip Chan, Amber L. Simpson, Richard K. G. Do
SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical Notes
Adam D. Lelkes, Eric Loreaux, Tal Schuster, Ming-Jun Chen, Alvin Rajkomar