自动机器学习模型和特征工程的有效性,用于诊断模糊尾炎,使用临床和计算机断层扫描的发现
Juho An1, Il Seok Kim2, Kwang-Ju Kim3
1Department of Emergency Medicine, Ajou University School of Medicine, World Cup-ro, Suwon, Gyeonggi-do, 16499, South Korea.
Scientific reports
|September 30, 2024
概括
自动机器学习 (AutoGluon) 与自动功能工程 (autofeat) 改善了对不确定的急性尾炎 (AA) 病例的诊断. 将临床数据和CT扫描与这些工具相结合,比传统方法显著提高了诊断准确度.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 人工智能在医学中的应用
- 计算生物学 计算生物学
背景情况:
- 诊断急性尾炎 (AA) 可能具有挑战性,尤其是在计算机断层扫描 (CT) 结果模两可的情况下.
- 现有的诊断方法,包括评分系统和单一机器学习模型,在不确定的情况下存在局限性.
- 需要改进诊断策略,以提高准确性和患者的治疗结果.
研究的目的:
- 为了评估自动机器学习 (AutoGluon) 与急性尾炎 (AA) 的自动化特征工程 (autofeat) 结合的诊断效果.
- 将AutoGluon模型的性能 (仅使用临床数据和联合临床-CT数据) 与传统的单一机器学习模型和成人尾炎评分 (AAS) 进行比较.
- 确定改善AA诊断的新型特征在成人患者中,CT结果不确定.
主要方法:
- 对303名成年患者进行了回顾性分析,对AA的CT检测结果不明.
- 应用AutoGluon和autofeat用于使用临床数据和联合临床-CT数据的预测建模.
- 使用接收器操作特征曲线 (AUROC) 下面的区域,精度,灵敏度,特异性,PPV,NPV和F1得分与逻辑回归,其他单一ML模型和AAS进行诊断性能比较.
主要成果:
- 该AutoGluon临床模型实现了0.785的AUROC,仅使用临床数据,其表现优于回归 (AUROC 0.755).
- 该AutoGluon-clinical-CT模型表现出优异的性能,AUROC为0.886,明显优于使用两种数据类型的回归模型 (AUROC 0.852,p=0.029).
- 一个新的预测特征,从疼痛到CT的持续时间 (exp) +反弹敏感性),被确定为具有显著重要性 (0.049,p=0.001).
结论:
- 自动机器学习 (AutoGluon) 和自动功能工程 (autofeat) 显著提高了不确定的急性尾炎 (AA) 病例的诊断.
- 将临床和CT发现整合到AutoML框架内,为提高诊断准确性提供了一个有希望的方法.
- 这些发现支持AutoML和autoFE的临床整合,以改进诊断策略,改善患者的治疗结果,并优化医疗保健资源的利用.
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