NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Jan. 14, 2025 Ryan Bell
Comprehensive analysis of the YprA-like helicase family provides deep insight into the evolution and potential mechanisms of widespread and largely uncharacterized prokaryotic antiviral defense systems -
Jan. 16, 2025 Qingqing Zhu
GPTRadScore and CT-Bench: Advancing Multimodal AI Evaluation and Benchmarking in CT Imaging -
Jan. 17, 2025 Xuegong Zhang
Using Large Cellular Models to Understand Cell Transcriptomics Language -
Jan. 21, 2025 Qiao Jin
Artificial Intelligence for Evidence-based Medicine -
Jan. 28, 2025 Kaleb Abram
TBD
RECENT SEMINARS
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Jan. 14, 2025 Ryan Bell
Comprehensive analysis of the YprA-like helicase family provides deep insight into the evolution and potential mechanisms of widespread and largely uncharacterized prokaryotic antiviral defense systems -
Dec. 17, 2024 Joey Thole
Training set associations drive AlphaFold initial predictions of fold-switching proteins -
Dec. 10, 2024 Amr Elsawy
AI for Age-Related Macular Degeneration on Optical Coherence Tomography -
Dec. 3, 2024 Sarvesh Soni
Toward Relieving Clinician Burden by Automatically Generating Progress Notes -
Nov. 19, 2024 Benjamin Lee
Reiterative Translation in Stop-Free Circular RNAs
Scheduled Seminars on Dec. 5, 2023
Speaker
Gaetano Manzo
Time
11 a.m.
Presentation Title
How do Clinicians Feel? A Trajectory Analysis of Clinical Notes using Large Language Models
Location
Virtual, see link below
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Clinical notes can provide insight into caregiver attitudes and how they impact patient care and satisfaction. However, detecting clinician attitudes from the language used in clinical notes is a challenging task, given the concise and standardized format of clinical notes and other contextual factors. In this study, we leverage multiple large language models to identify clinician attitudes from the linguistic features in clinical notes. This approach promises to provide a reliable means of improving patient care, clinician well-being, and communication by identifying specific clinicians' attitude trajectories from clinical notes.