SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization
Year: 2024
Type: article
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, Cancer Prevention and Research Institute of Texas, University of Texas Health Science Center at Houston, National Institutes of Health, National Institute on Aging, National Human Genome Research Institute, National Cancer Institute, U.S. National Library of Medicine, National Center for Advancing Translational Sciences
Awards: Recruitment of Rising Stars, Open Health Natural Language Processing Collaboratory, Finding combinatorial drug repositioning therapy for Alzheimer's disease and related dementias, G5388406022, AIM-AHEAD Coordinating Center - All Four Cores, Collaborative Research: SaTC: CORE: Medium: PREMED: Privacy-Preserving and Robust Computational Phenotyping using Multisite EHR Data, RADx-Rad Discoveries & Data: Consortium Coordination Center Program Organization, Harmonizing multiple clinical trials for Alzheimer's disease to investigate differential responses to treatment via federated counterfactual learning, G9000064959, G9000112157, G9000227001, G9000292904, Robust privacy preserving distributed analysis platform for cancer research: addressing biased, incomplete data, Decentralized differentially-private methods for dynamic data release and analysis, A FAIR Bridge2AI Center (FABRIC), Accelerating Genomic Data Sharing and Collaborative Research with Privacy Protection, AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learning
OpenAlex ID: W4391826804