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 GEN AI Framework for Medical Note Generation
Hui Yi Leong, Yi Fan Gao, Shuai Ji, Bora Kalaycioglu, Uktu Pamuksuz
Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models
Zehan Li, Yan Hu, Scott Lane, Salih Selek, Lokesh Shahani, Rodrigo Machado-Vieira, Jair Soares, Hua Xu, Hongfang Liu, Ming Huang