基于机器学习技术的复杂尾炎临床预测模型的构建
Wang Wei1, Shen Tongping2,3, Wang Jiaming4
1The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei, China.
Scientific reports
|July 16, 2024
概括
机器学习可以准确地诊断老年患者的急性尾炎. 梯度增强机 (GBM) 算法表现出卓越的性能,有助于及时有效的治疗计划.
科学领域:
- 老年人手术是一门老年手术.
- 医疗信息学 医疗信息学
- 机器学习在医学中的应用
背景情况:
- 急性尾炎是老年人常见的手术紧急情况,带来了诊断挑战.
- 准确的诊断对于有效的治疗规划和改善患者治疗结果至关重要.
研究的目的:
- 开发和验证一种简单,快速和准确的机器学习模型,用于老年患者急性尾炎的早期诊断.
- 为了比较各种机器学习算法用于尾炎诊断的性能.
主要方法:
- 对322名急性尾炎的老年患者 (2012-2022) 的临床数据的分析.
- 九种机器学习技术的实施和比较:LR,CART,RF,SVM,贝叶斯,KNN,NN,FDA和GBM.
- 使用SHAP解释最佳模型 (GBM),并通过校准和决策曲线分析临床效用.
主要成果:
- 梯度增强机 (GBM) 算法实现了最佳的诊断性能.
- 针对GBM的关键性能指标包括:灵敏度0.9167,特异性0.9739,精度0.9429,F1得分0.9296.
- 开发的Shiny应用程序证明了诊断复杂尾炎的临床和经济效益.
结论:
- 机器学习,特别是GBM算法,为诊断老年人急性尾炎提供了一个高度准确的方法.
- 开发的工具可以帮助临床医生做出急性尾炎的及时和明智的治疗决定.
- 该研究强调了人工智能在改善老年人群手术紧急情况的医疗保健提供方面的潜力.
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