Summary of Leveraging Large Language Models to Extract Information on Substance Use Disorder Severity From Clinical Notes: a Zero-shot Learning Approach, by Maria Mahbub et al.
Leveraging Large Language Models to Extract Information on Substance Use Disorder Severity from Clinical Notes: A Zero-shot Learning Approach
by Maria Mahbub, Gregory M. Dams, Sudarshan Srinivasan, Caitlin Rizy, Ioana Danciu, Jodie Trafton, Kathryn Knight
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel study employs Large Language Models (LLMs) to extract severity-related information from clinical notes for substance use disorder (SUD) diagnoses. The researchers propose a workflow combining zero-shot learning with carefully crafted prompts and post-processing techniques, demonstrating the superiority of LLMs over rule-based approaches using Flan-T5. This work contributes to improved risk assessment and treatment planning for SUD patients by accurately extracting severity information for 11 categories of SUD diagnoses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computers called Large Language Models (LLMs) to help doctors understand the severity of substance use disorders from medical records. Currently, doctors have to manually add extra details to notes to describe how severe a patient’s addiction is. The LLMs can quickly read and understand this information better than usual methods. By using these models, doctors might be able to make more accurate diagnoses and plan better treatment for patients. |
Keywords
» Artificial intelligence » T5 » Zero shot
