Summary of Clinicalagent: Clinical Trial Multi-agent System with Large Language Model-based Reasoning, by Ling Yue and Sixue Xing and Jintai Chen and Tianfan Fu
ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning
by Ling Yue, Sixue Xing, Jintai Chen, Tianfan Fu
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The proposed Clinical Agent System (ClinicalAgent) is an integrated solution that leverages Large Language Models (LLMs), multi-agent architectures, and advanced clinical trial tools to enhance the accessibility and utility of clinical trials. The system, designed for clinical trial tasks, combines GPT-4 with LEAST-TO-MOST and ReAct reasoning technology to boost LLM performance in clinical contexts. ClinicalAgent achieves competitive predictive performance in clinical trial outcome prediction (0.7908 PR-AUC), outperforming the standard prompt method by 0.3326. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Clinical Agent System is a new tool that helps make medical studies more effective. It combines special computer models and problem-solving techniques to predict the outcomes of clinical trials better than usual methods. This system can help researchers make more accurate predictions about the results of their studies, which could lead to better treatments and care for patients. |
Keywords
» Artificial intelligence » Auc » Gpt » Prompt
