轻量级可解释的AI模型使用多个血液参数用于急性尾炎的紧急诊断
Shun Liao1, Yan Li1, Haoran Tang2
1Key Laboratory of Cyber-Physical Power System of Yunnan Colleges and Universities, School of Electrical and Information Engineering, Yunnan Minzu University, Kunming, China.
这项研究开发了一种可解释的机器学习模型,使用常规血液测试快速诊断急性尾炎. 该模型准确地识别尾炎,有助于及时治疗,特别是在资源有限的环境中.
科学领域:
- 医疗信息学 医疗信息学
- 血液学 血液学 血液学
- 机器学习 机器学习
背景情况:
- 由于症状与其他腹部疾病重叠,急性尾炎的诊断具有挑战性.
- 误诊或延迟诊断尾炎可能导致并发症.
- 对于急性尾炎,需要快速准确的诊断工具.
研究的目的:
- 开发和验证可解释的机器学习模型,用于诊断急性尾炎.
- 在模型开发中使用常规的血液学指标.
- 为了促进急性尾炎的及时和准确诊断.
主要方法:
- 对408名急性腹痛患者的回顾性分析.
- 应用LASSO,ElasticNet和随机森林来进行特征选择.
- 在一个独立的测试集上开发和评估11个机器学习分类器.
主要成果:
- 带有辐射基函数内核 (SVM-RBF) 的支向量机实现了0.903.3的AUC.
- 发现的关键预测因素是中性细胞百分比 (NE%),乙氨基细胞百分比 (EO%),红细胞 (RBC) 和白细胞 (WBC) 计数.
- 解释性分析证实EO%,RBC和WBC是最有影响力的预测因素.
结论:
- 使用常规血液指标的节和可解释的机器学习模型可以帮助诊断急性尾炎.
- 该模型为及时和准确的诊断提供了潜力,特别是在资源有限的环境中.
- 这种方法为尾炎病例的临床决策提供了宝贵的见解.
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