使用人工智能的症分类:一个基于Web的应用程序
Ali A Asadi-Pooya1,2, Davood Fattahi1, Nahid Abolpour1
1Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Epilepsia open
|August 11, 2023
概括
机器学习使用临床数据准确地区分异常发病性普遍性 (IGE) 与焦点性. 该工具有助于对10岁及以上患者的类型进行分类.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 的分类对于有效治疗至关重要.
- 使用传统方法,区分异常性泛性 (IGE) 和焦点性可能是具有挑战性的.
- 机器学习 (ML) 提供了提高诊断准确性的潜力.
研究的目的:
- 评估使用临床信息用于基于ML的IGE和焦点之间区分的可行性.
- 开发一种可靠的ML模型来对进行分类.
主要方法:
- 对前性维护数据库 (2008-2022) 的回顾性分析.
- 包括电临床诊断为IGE或焦点的患者.
- 数据集分为70%的培训和30%的测试子集.
- 采用了将多个分类器结合起来进行最终分类的堆叠方法.
主要成果:
- 研究了1445名患者 (964名焦点,481名IGE).
- 堆叠分类器的表现优于基础分类器.
- 获得了0.81的精度,0.81的灵敏度和0.77.71的特异性.
结论:
- 开发了一种务实的ML算法,用于10岁以上患者的症分类.
- 该ML模型在线可用于外部验证和临床使用.
- 促进更准确和更容易获得的诊断.
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