使用机器学习方法预测急性尾炎的严重程度:对于临床医生来说,这是一个简单而有前途的方法
Hilmi Yazici1, Onur Ugurlu2, Yesim Aygul3
1General Surgery Department, Marmara University Pendik Research and Training Hospital, Istanbul, Turkey. hilmiyazici@hotmail.com.
BMC emergency medicine
|June 17, 2024
概括
机器学习准确地预测了急性尾炎的严重程度,仅使用年龄,C-反应蛋白 (CRP) 和围尾液收集 (PALC). 这种模型为临床实践提供了高准确度.
科学领域:
- 医疗信息学 医疗信息学
- 手术紧急情况 手术紧急情况
- 机器学习在医学中的应用
背景情况:
- 急性尾炎 (AA) 是一个常见的全球手术紧急情况.
- 区分简单和复杂的AA是临床上有意义的.
- 机器学习 (ML) 提供了提高诊断准确性的潜力.
研究的目的:
- 评估6个ML算法对急性尾炎的预测性能.
- 为了比较ML模型来区分简单与复杂的AA.
- 为了确定AA严重程度的关键预测因素.
主要方法:
- 对1132名经过手术的AA患者 (2012-2022) 的回顾性分析.
- 患者分为穿孔和非穿孔AA组.
- 使用统计学上显著特征 (p<0.05) 训练和验证ML模型,包括年龄,CRP和PALC.
主要成果:
- 在验证集中,K-近邻和后勤回归实现了96%的准确性.
- 确定了穿孔AA的关键预测因素:年龄,C反应蛋白 (CRP) 和围尾液体收集 (PALC).
- 多变量分析证实了年龄,CRP和PALC的重要性.
结论:
- 一个高度准确的预测模型AA严重性被开发使用只有三个特征.
- 该模型在急性尾炎的临床决策中显示出潜在的实用性.
- 机器学习算法在改善手术紧急情况的诊断方面表现有前途.
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