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用于亚临床疾病评估的生理基础建模:一个潜在的试点模型
William Yuan1, Shiwei Xu1, Sara Dionisi2
1Etiome Inc, Cambridge, MA, 02142, United States.
JAMIA open
|January 28, 2026
概括
一个新的人工智能驱动的"生物概况"准确地识别了患者的脂肪性肝病研究. 这种方法显著减少了查需求,并通过检测亚临床疾病特征来提高试验精度.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能在医学中的应用
- 临床试验招聘 临床试验招聘
背景情况:
- 临床试验招聘面临的挑战是由于未被诊断的患者和缺乏特定标签.
- 有效的患者鉴定对于临床研究的成功和质量至关重要,特别是对于像脂肪性肝病这样的疾病.
研究的目的:
- 开发和前性测试一种基于生理学的患者表征,称为"生物形状",用于改善临床研究中的患者识别.
- 评估生物档案是否可以减少查数量,并使化性肝病研究的候选人能够更精确地定位.
主要方法:
- 训练有素的生物形状模型使用从100多万名受试者定期收集的健康数据.
- 对多个终点进行了微调模型,并将其应用于45484名研究对象的队列.
- 对前性研究数据进行了验证的生物形状预测,并将性能与现有的基础模型和临床风险得分进行了比较.
主要成果:
- 生物形状模型与质子密度脂肪分数 (PDFF) 相比,达到0.65的斯皮尔曼系数,优于现有方法 (ρ=0.361-0.542).
- 模拟表明,与目前的方法相比,生物概况可以将需要查的受试者数量减少一半.
- 展望验证显示,生物形状预测与研究数据 (ρ=0.740) 之间有很强的对齐.
结论:
- 基于人工智能的生物分析可以识别具有亚临床疾病特征的个体,提高临床试验质量.
- 广泛实施可以减少查失败,并识别具有特定条件的以前未知的受试者.
- 生物档案显示出在精准医学和人工智能增强医疗保健方面的决策支持潜力.
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