在紧急结直肠外科手术中基于机器学习的术后死亡率预测:使用Tokushukai医学数据库进行了回顾性多中心队列研究
Shota Akabane1,2,3, Katsunori Miyake4,5, Masao Iwagami6,7
1Department of Urology, Tokyo Women's Medical University, 8-1, Kawadacho, Shinjuku City, Tokyo, Japan.
Heliyon
|October 9, 2023
概括
机器学习模型准确地预测紧急结直肠手术患者的住院死亡率. 关键预测因素包括年龄,结肠直肠癌,腹腔镜使用和特定的实验室值,有助于资源配置.
科学领域:
- 医疗信息学 医疗信息学
- 手术瘤学手术瘤学
- 医疗保健中的机器学习
背景情况:
- 准确的死亡风险评估对于紧急结直肠外科手术的资源配置至关重要.
- 现有的预后因素需要改进,以便精确地对患者进行分层.
- 机器学习为提高死亡率预测提供了一个有希望的方法.
研究的目的:
- 开发和验证机器学习模型,用于预测紧急结直肠手术患者的住院死亡率.
- 使用机器学习识别与住院死亡率相关的显著预后因素.
- 改善高风险患者的临床决策和资源管理.
主要方法:
- 对8792名接受紧急结直肠手术 (2012-2020) 的成年患者进行了回顾性队列研究.
- 逻辑回归,随机森林,梯度增强决策树 (GBDT) 和多层感知子 (MLP) 模型的比较.
- 使用沙普利增量解释 (SHAP) 来识别关键预测变量.
主要成果:
- 梯度增强决策树 (GBDT) 实现了最高的预测性能,AUROC为0.814.
- 死亡率的显著预测因素包括晚年,结肠直肠癌,腹腔镜方法,血清乳酸脱酶升高,血清白蛋白低,以及高血尿酸.
- 机器学习模型在预测住院死亡率方面表现出强大的准确性.
结论:
- 基于GBDT的机器学习模型为紧急结直肠手术的医院死亡率提供了优异的预测.
- 鉴定的预后因素可以指导临床干预和资源分配.
- 机器学习模型在这个患者群体中具有显著的风险分层潜力.
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