Summary of Bespoke Large Language Models For Digital Triage Assistance in Mental Health Care, by Niall Taylor et al.
Bespoke Large Language Models for Digital Triage Assistance in Mental Health Care
by Niall Taylor, Andrey Kormilitzin, Isabelle Lorge, Alejo Nevado-Holgado, Dan W Joyce
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper investigates the application of contemporary large language models (LLMs) to process unstructured clinical data in electronic health records (EHRs), with a focus on mental health. The authors highlight the importance of utilizing LLMs for processing narrative free-text EHR data, as most patient information is currently lacking structured machine-readable content. The paper aims to leverage the capabilities of LLMs to improve the analysis and utilization of unstructured clinical data in mental health applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how big language models can help process medical records that are written in free-form text. This is especially important for mental health, where most information isn’t organized in a way that computers can easily understand. The goal is to use these language models to make it easier to analyze and use this unstructured data to improve mental health care. |
