图表式基础模型作为当地手术风险预测的新可移植标准
Chris Varghese1, Elizabeth Habermann2, Kristine Hanson3
1Division of Hepatobiliary and Pancreas Surgery, Mayo Clinic, Rochester, MN; Department of Surgery, University of Auckland, Auckland, NZ. Electronic address: https://twitter.com/chrisvarghese98.
Surgery
|February 10, 2026
概括
一个新的表式基础模型显著提高了急诊手术中手术风险预测的准确性. 这种人工智能方法增强了当地风险分层,即使在有限的特定站点数据下,也提供了更好的性能.
科学领域:
- 人工智能在医学中的应用
- 手术结果研究研究.
- 医疗信息学 医疗信息学
背景情况:
- 手术风险分层通常是局部特定的,全球基础设施有限,用于准确的手术前评估.
- 在大型合成数据集上训练的基础模型为域特定风险预测提供了潜力.
- 评估表式基础模型在紧急手术中对局部风险分层的实用性至关重要.
研究的目的:
- 评估基于变压器的表式基础模型在改善紧急手术局部风险分层方面的作用.
- 为了比较基础模型的性能与传统方法,如物流回归和梯度增强.
- 确定模型在各种特定地点的队列中的有效性,包括那些数据有限的队列.
主要方法:
- 基于变压器的表式基础模型应用于来自美国外科医生学院国家外科质量改进计划数据库的机构数据.
- 性能与后勤回归和XGBoost模型进行了比较.
- 比较包括整体性能,特定站点的队列 (n=5),以及在单站与多站数据上训练的模型.
- 评估的关键结果是30天死亡率和发病率.
主要成果:
- 表式基础模型在预测30天的发病率和死亡率方面表现出卓越的表现,在接收器操作特征曲线 (0.82-0.89) 和精度回忆曲线 (0.35-0.68) 下获得更高的区域.
- 这款车型表现出极好的校准,与传统车型相比,布赖尔分数较低 (0.04-0.15).
- 它的高性能在较小的特定站点数据集中保持不变,单站点训练模型的性能与多站点训练的传统模型相比.
结论:
- 图表式基础模型可以提高获得高性能外科风险分层的机会.
- 这种便携式方法有效地处理缺失的数据,并显示出强大的比较性能,即使在较小的数据集中.
- 基础模型为改善全球紧急手术中手术前风险评估提供了一个有希望的途径.
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