人工智能区分了腹部亨诺克-施恩莱因紫与儿童急性尾炎的区别
Dan Nie1, Yishan Zhan2, Kun Xu2
1Department of General Surgery, Jiangxi Provincial Children's Hospital, Nanchang, Jiangxi, China.
International journal of rheumatic diseases
|October 31, 2023
概括
这项研究开发了一种人工智能 (AI) 模型,以区分儿科腹部黑诺赫-舒恩莱因紫 (AHSP) 和急性尾炎 (AA). XGBoost AI 模型实现了高精度,为临床诊断提供了有前途的工具.
科学领域:
- 儿科胃肠病学 儿科胃肠病学
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 在儿童中,区分腹部亨诺赫-施恩莱因紫外线 (AHSP) 和急性尾炎 (AA) 存在诊断挑战.
- 准确及时诊断对于适当的治疗和预防并发症至关重要.
研究的目的:
- 开发和验证人工智能 (AI) 模型,以区分儿科患者的AHSP与AA.
- 确定帮助区分这两个条件的关键实验室指标.
主要方法:
- 使用了6965名儿科患者 (2201名AHSP,4764名AA) 的数据集,并使用了53个实验室指标.
- 开发并比较了五种机器学习模型:XGBoost,AdaBoost,高斯天真贝叶斯,MLPClassifier和支持矢量机.
- 使用ROC曲线分析,校准曲线和决策曲线分析评估模型性能.
主要成果:
- 确定了32个显著的歧视性指标,其中淋巴细胞比率,乙素比率/计数,中性粒细胞比率和CRP表现强 (AUC ≥0.80).
- 在验证组中,XGBoost模型表现出卓越的性能,具有最高的AUC (0.895),精度 (0.824),和卡帕值 (0.621).
- 优化的XGBoost模型实现了显著的诊断性能 (AUC>0.95),表明了显著的临床效用.
结论:
- 基于人工智能的机器学习模型可以准确地区分儿科患者的AHSP和AA.
- 开发的XGBoost模型显示了高预测准确性和临床实用性,支持数据驱动的诊断决策.
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