机器学习在检测儿科发作中的准确性:系统审查和元分析
Zhuan Zou1,2, Bin Chen1,2, Dongqiong Xiao1,2
1Department of Emergency, West China Second University Hospital, Sichuan University, Chengdu, China.
Journal of medical Internet research
|December 11, 2024
概括
机器学习 (ML) 和深度学习 (DL) 显示出使用脑电图数据监测儿科发作的前景. DL模型的准确性高于ML,支持在未来早期预警工具开发中使用它们.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 实时监测儿科发作是临床上具有挑战性的.
- 机器学习 (ML) 显示出诊断和治疗神经系统疾病的潜力,包括儿科.
- 关于ML在儿科发作检测中的可行性的系统证据是有限的.
研究的目的:
- 系统地审查和巩固关于ML在监测儿科发作中的有效性证据.
- 为开发用于发作检测的智能工具提供基于证据的基础.
- 为了比较ML和深度学习 (DL) 在儿科监测中的表现.
主要方法:
- 在PubMed,Cochrane,Embase和Web of Science数据库中进行系统搜索.
- 包含2023年8月27日之前对儿童发作检测的ML的原始研究.
- 使用QUADAS-2和对诊断准确度指标 (C指数,灵敏度,特异性,准确度) 的元分析进行偏差风险评估.
主要成果:
- 包括28项研究 (15ML,13DL),所有这些都使用了电脑图数据.
- ML汇总的C指数:0.76 (培训),0.73 (验证).
- DL验证C指数:0.91,灵敏度为0.89,特异性为0.91,精度为0.89.
结论:
- 人工智能方法,特别是DL,在儿童发病检测方面显示出有希望的准确性.
- 与传统的ML模型相比,深度学习模型提供了更高的检测精度.
- 这些发现支持开发基于DL的儿童发作早期预警系统.
关键词:
这是一个EEGEEGEEGEEGEEGEEGEEG.孩子们的孩子们的孩子们的孩子们.深度学习是一种深度学习.检测 检测 检测 检测 检测一个电脑电图 (electroencephalogram) 是一个电脑电图.是一种.发作 发作 发作机器学习是机器学习.儿科 儿科 儿科 儿科更多相关视频
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