一个可解释和透明的机器学习框架,用于在儿科患者中检测尾炎.
Krishnaraj Chadaga1, Varada Khanna2, Srikanth Prabhu3
1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
Scientific reports
|October 18, 2024
概括
这项研究引入了一个人工智能模型来诊断儿童尾炎,达到94%的准确性. 诊断的关键指标包括尾直径和白细胞计数.
科学领域:
- 医疗信息学 医疗信息学
- 儿科手术 儿科手术
- 人工智能在医学中的应用
背景情况:
- 尾炎是一种常见的儿科疾病,需要及时诊断,以预防腹膜炎和败血症等严重并发症.
- 目前的诊断方法涉及实验室测试和成像,需要提高准确性和速度.
- 人工智能 (AI) 和机器学习为改善医疗诊断提供了有希望的工具.
研究的目的:
- 开发和评估监督学习模型,以准确诊断儿科患者的尾炎.
- 为了比较各种超参数调技术的性能,以优化AI诊断模型.
- 通过使用可解释的人工智能,确定可预测尾炎的关键临床和成像标志物.
主要方法:
- 使用了六种不同的搜索技术 (贝叶斯优化,混合蝙蝠算法等). 用于超参数调.
- 训练并评估了9种用于儿童尾炎检测的分类模型.
- 应用了五种可解释的AI技术来解释模型预测和识别重要的诊断特征.
主要成果:
- 混合蝙蝠算法通过定制APPSTACK模型实现了最高准确率 (94%).
- 可解释的人工智能识别了关键的诊断标记,包括停留时间,超声波形尾检测,白细胞计数和尾直径.
- 开发的AI系统展示了早期和验证的尾炎诊断的潜力.
结论:
- 人工智能,特别是混合蝙蝠算法,在诊断儿科尾炎方面表现出很高的有效性.
- 尾直径和白细胞计数等关键特征对于人工智能驱动的尾炎检测至关重要.
- 拟议的AI系统可以帮助临床医生进行快速可靠的诊断,并补充现有方法.
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