提高术前结果预测:对机器学习进行比较的回顾性病例对照研究与国际ESodata研究小组风险模型,用于预测瘤性食道切除术90天死亡率
Axel Winter1, Robin P van de Water2, Bjarne Pfitzner2
1Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany.
Cancers
|September 14, 2024
概括
机器学习 (ML) 模型在预测瘤食道切除术后90天死亡率方面表现优异,与国际ESodata研究小组 (IESG) 风险模型相比. ML为手术决策提供了更好的风险分层.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 预测外科手术风险预测
背景情况:
- 术前风险预测对于瘤性食道切除术的明智决策至关重要.
- 国际Esodata研究小组 (IESG) 开发了一种90天死亡率的风险模型.
- 由于复杂的,非线性风险因素相互作用,机器学习 (ML) 提供了一种新的方法.
研究的目的:
- 评估IESG风险模型的表现.
- 将IESG模型与ML模型进行比较,以预测食道切除术后的90天死亡率.
- 评估ML在改善手术前风险分层方面的潜力.
主要方法:
- 在两个独立中心的552名患者中培训和验证了多个ML分类器.
- 通过使用接收器操作特征曲线下的面积 (AUROC),精度回调曲线下的面积 (AUPRC) 和马修斯相关系数 (MCC) 来评估模型歧视.
- 将ML模型的性能与IESG风险分类进行比较.
主要成果:
- 整体90天死亡率为5.8%.
- 该IESG模型提供了足够的基于组的风险预测.
- ML模型显示显著优异的歧视:更高的AUROC (0.64比0.44),AUPRC (0.25比0.11) 和MCC (0.27比0.15).
结论:
- ML模型显示了在食道切除术前识别高风险患者的有希望的潜力,优于传统的统计模型.
- 与IESG模型相比,ML提供了增强的歧视.
- 为了未来的大规模临床实施和更高的预测准确性,需要更大的数据集.
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