Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning
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View abstract on PubMed
Summary
This summary is machine-generated.Generative large language models (LLMs) can effectively summarize doctor-patient dialogues using prompt-tuning. This efficient method offers a low-cost solution for automatic text summarization in clinical settings.
Area Of Science
- Artificial Intelligence
- Clinical Informatics
- Natural Language Processing
Background
- Automatic text summarization (ATS) is crucial for seamless clinical care.
- Generative large language models (LLMs) offer potential for summarizing complex medical dialogues.
Purpose Of The Study
- To develop and evaluate a prompt-tuning approach for summarizing doctor-patient dialogues using generative LLMs.
- To assess the performance of GatorTronGPT, a clinical LLM, against a T5-based fine-tuning model.
Main Methods
- Developed prompt-tuning algorithms to guide generative LLMs for clinical text summarization.
- Examined prompt-tuning strategies, soft prompt sizes, and few-shot learning capabilities.
- Utilized the MTS-DIALOG clinical benchmark dataset for evaluation.
Main Results
- The GatorTronGPT-20B model demonstrated superior performance across all evaluation metrics.
- Prompt-tuning proved to be computationally efficient, as LLM parameters remained unchanged.
- The approach effectively summarized clinical text, showcasing the utility of generative clinical LLMs.
Conclusions
- Generative clinical LLMs, when guided by prompt tuning, are efficient for automatic text summarization.
- This method provides a low-cost and effective solution for summarizing doctor-patient dialogues.
- The study highlights the potential of prompt tuning for advancing clinical informatics.
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