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儿童尾炎的机器学习和特征选择

John Kendall¹, Gabriel Gaspar¹, Derek Berger¹

¹Department of Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada.

Tomography (Ann Arbor, Mich.)

|August 27, 2025

概括

机器学习可以准确预测儿童尾炎的诊断,治疗和严重程度. 超声波功能可以提高诊断的准确性,但对于预测治疗结果或严重程度至关重要.

科学领域:

医疗信息学
医学的人工智能
儿童手术

背景情况:

准确预测儿科尾炎的诊断,治疗和严重程度对于有效的临床决策至关重要.
机器学习 (ML) 模型有助于提高诊断准确性和患者的治疗结果.
超声波 (US) 图像描述特征在ML模型性能中的作用需要进一步研究.

研究的目的:

评估各种ML模型和儿童尾炎特征选择技术的预测性能.
评估美国图像描述特征对模型性能和可解释性的影响.
确定最佳的ML方法来预测儿科尾炎的诊断,治疗和严重程度.

主要方法:

781名患有尾炎的儿科患者 (0 - 18岁) 的回顾性队列研究.
开发和验证ML模型,包括随机森林,物流回归,SGD和LGBM.
基于过器,嵌入式和封装特征选择方法的ML模型的详尽配对,包括一种新的冗余意识方法.
使用精度和AUROC指标对具有或没有美国图像描述特征的模型进行评估.

主要成果:

美国的特点是显著提高诊断准确性,减少模型偏差.
模型实现了高性能:诊断 (随机森林与LGBM特征选择:98.1%准确率,0.993 AUROC),管理 (随机森林:93.9%准确率,0.980 AUROC) 和严重程度 (LGBM以过器为基础的特征选择:90.1%准确率,0.931 AUROC).
美国的特征对于最大限度地准确地预测管理或严重程度并不重要.

结论:

高性能,可解释的ML模型可以有效预测儿科尾炎的关键临床结果.
美国的图像特征提高了诊断准确性,但对于预测管理或严重性而言并不重要.
在儿童尾炎的临床决策优化方面提供了一个有希望的工具.

关键词:

尾炎进行分类机器学习儿童医学预测医学

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