改善放射治疗后的死亡率预测,使用大规模非结构化电子健康记录的大型语言模型结构
Sangjoon Park1, Chan Woo Wee2, Seo Hee Choi2
1Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea.
概括
大型语言模型 (LLM) 准确地构建电子健康记录,显著改善放射治疗患者的生存预测. 这增强了临床决策和治疗优化.
科学领域:
- 在瘤学中使用人工智能
- 临床数据科学 临床数据科学
- 医疗信息学 医疗信息学
背景情况:
- 精确的患者选择对于避免在寿命有限的患者中不必要的放射治疗至关重要.
- 由于结构化数据分析的局限性,传统的生存模型难以准确.
- 大型语言模型 (LLM) 提供了一种用于结构化非结构化电子健康记录 (EHR) 数据的新方法.
研究的目的:
- 评估一般领域的LLM在结构化非结构化EHR数据以预测生存的有效性.
- 将LLM的绩效与特定领域的模型和传统方法进行比较.
- 评估LLM结构化数据对生存预测模型的准确性和可解释性的影响.
主要方法:
- 分析了34276名放射治疗患者的结构化和非结构化EHR数据.
- 使用开源的LLM来单次结构化非结构化的EHR数据.
- 对852名患者进行外部验证,并与特定领域的LLM和较小变体进行比较.
- 开发生存预测模型,使用统计,机器学习和深度学习方法结合结构化和LLM结构化数据.
主要成果:
- 开源的LLM在结构化EHR数据方面实现了87.5%的准确性,显著优于特定领域的医学LLM (35.8%).
- 包括一般状况和疾病程度在内的LLM结构特征与生存结果有很强的相关性.
- 纳入LLM结构化数据改善了深度学习模型的性能 (C指数从0.737到0.820内部,0.779到0.842外部) 和风险分层.
结论:
- 一般领域的LLM可以在没有医疗微调的情况下有效地构建大型非结构化EHR,从而提高生存预测的准确性.
- 通过将关键特征与传统预测器对齐,LLM集成提高了模型的解释性.
- 该RT-Surv框架证明了LLMs在改善临床决策和优化放射治疗方面的潜力.
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