大型语言模型预测患者健康轨迹,使数字双胞胎成为可能
Nikita Makarov1,2,3, Maria Bordukova1,2,3, Papichaya Quengdaeng2,4
1Roche Innovation Center Munich (RICM), Penzberg, Germany.
NPJ digital medicine
|October 1, 2025
概括
生成型人工智能 (AI) 和大型语言模型 (LLM) 创建了先进的数字双胞胎,用于预测患者的健康轨迹. 该DT-GPT模型使用电子健康记录准确预测临床结果,优于现有方法.
科学领域:
- 人工智能的人工智能
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
背景情况:
- 生成型人工智能正在改变数字双胞胎技术,为健康预测创造虚拟患者模型.
- 大型语言模型 (LLM) 显示出临床预测应用的巨大潜力.
研究的目的:
- 开发和评估用于临床轨迹预测的数字双胞胎生成预训练变压器 (DT-GPT).
- 将基于LLM的预测扩展到复杂的医疗保健数据挑战.
主要方法:
- 开发了DT-GPT,这是临床预测的LLM扩展.
- 使用电子健康记录 (EHR) 没有归算或规范化.
- 在各种数据集上与最先进的机器学习模型进行基准测试.
主要成果:
- 在非小细胞肺癌,ICU和阿尔茨海默病数据集上,DT-GPT的表现优于现有的模型,分别减少了3.4%,1.3%和1.8%的缩放平均绝对误差.
- 该模型保留了临床变量分布和交叉相关性.
- 通过人类可解释的界面和零射击预测能力来证明可解释性.
结论:
- DT-GPT提供了一种可靠的解决方案,用于使用EHR进行临床轨迹预测,克服数据限制.
- 作为临床预测的平台,LLM表现有前途,在试验,治疗选择和不良事件缓解方面具有潜在的应用.
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