一种基于变压器的生存模型,用于预测心力衰竭患者全因死亡率:一项多队列研究
Shishir Rao1, Nouman Ahmed2, Gholamreza Salimi-Khorshidi2
1Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK. Shishir.rao@wrh.ox.ac.uk.
NPJ digital medicine
|January 8, 2026
概括
一个新的人工智能模型,TRisk,使用电子健康记录准确地预测心力衰竭患者的死亡率. 特里斯克的表现优于现有模型,并显示出更好的风险分层的潜力.
科学领域:
- 人工智能在医学中的应用
- 心血管疾病研究研究
- 医疗信息学 医疗信息学
背景情况:
- 现有的心力衰竭 (HF) 患者的风险模型往往无法捕捉到他们复杂的健康状况.
- 常规的电子健康记录 (EHR) 包含有价值的数据来预测HF患者的结果.
- 在心力衰竭管理中需要改进死亡率预测模型.
研究的目的:
- 开发和验证基于变压器的人工智能生存模型TRisk,用于预测心力衰竭患者的死亡率.
- 使用常规EHR数据,将TRisk的性能与MAGGIC-EHR模型进行比较.
- 评估TRisk在不同医疗保健环境中的通用性.
主要方法:
- 开发TRisk,一个基于变压器的AI生存模型,利用常规EHR数据.
- 在英国的大型队列 (403,534名高温肺炎患者) 上进行培训和内部验证.
- 在美国队列 (21,767名HF患者) 上进行外部验证,并与MAGGIC-EHR进行比较.
主要成果:
- 与MAGGIC-EHR (C指数:0.728) 相比,TRisk在英国队列中实现了更高的36个月死亡率预测一致性指数 (C指数:0.845).
- 与MAGGIC-EHR相比,TRisk在各个子组 (性别,年龄,基线特征) 的偏差较小.
- 美国队列的外部验证得出0.802的C指数,可解释性确定了癌症和肝衰竭等新型风险因素.
结论:
- 使用常规EHR数据,TRisk为心力衰竭患者提供了更准确和精确的死亡率预测.
- 该模型显示了在国际环境中改善心力衰竭护理风险分层的潜力.
- TRisk能够识别既确定的,又被低估的预后因素,增强临床决策.
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