叙事特征还是结构特征? 一项大型语言模型研究,以识别心力衰竭风险的癌症患者
Ziyi Chen1, Mengyuan Zhang1, Mustafa Mohammed Ahmed2
1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
概括
鉴定心力衰竭 (HF) 风险的癌症患者至关重要. 分析电子健康记录的大型语言模型 (LLM) 在预测HF风险方面表现有前途,改善了癌症患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 心脏病学 心脏病学
- 人工智能的人工智能
背景情况:
- 癌症疗法可能会导致心脏毒性,影响患者的生存.
- 在癌症患者中早期发现心力衰竭 (HF) 风险对于改善结果至关重要.
研究的目的:
- 评估机器学习 (ML) 模型,以预测癌症患者的HF风险.
- 探索大型语言模型 (LLM) 的实用性,其中包含来自电子健康记录 (EHR) 的新叙事特征.
主要方法:
- 利用佛罗里达大学健康学院12806名癌症患者 (肺癌,乳腺癌,结直肠癌) 的电子病历数据.
- 传统的ML,时间意识长期短期记忆 (T-LSTM) 和LLMs (GatorTron-3.9B) 的比较.
- 结合了从结构化医疗代码中获得的新叙事特征.
主要成果:
- 1,602名患者在癌症后诊断时发展出HF.
- 该LLM (GatorTron-3.9B) 获得了优异的F1分数,比SVM高出39%,比T-LSTM高出7%,比BERT高出5.6%.
- 叙述特征显著提高了模型性能和特征密度.
结论:
- 在癌症患者中,LLM在预测HF风险方面具有显著的潜力.
- 整合来自EHR的叙事特征可以提高HF风险预测模型的准确性.
- 这种方法可以提高癌症治疗的安全性和结果.
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