评估机器学习算法的有效性:一个系统的审查.
Choon-Hian Goh1,2, Mahbuba Ferdowsi1,2, Ming Hong Gan1
1Department of Mechatronics and BioMedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia.
MethodsX
|January 1, 2024
概括
机器学习 (ML) 算法通过分析来自Head-up Tilt Table Tests (HUTT) 的血液动力学数据来显著提高昏迷诊断. 这些ML方法比传统的评分系统提供了更好的准确性,有助于改善患者的治疗结果.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 生物医学工程 生物医学工程
背景情况:
- 昏迷,突然的,暂时的意识丧失,提出了诊断挑战.
- 对于昏迷的现有诊断方案可能是不理想的.
- 机器学习 (ML) 提供了提高诊断准确性的潜力.
研究的目的:
- 系统地评估机器学习 (ML) 算法用于昏迷诊断.
- 为了比较ML算法的性能与传统的点评协议.
- 评估ML在分析血动力学参数时的实用性,在头上倾斜表测试 (HUTT).
主要方法:
- 在IEEE Xplore,Web of Science和Elsevier的系统文献搜索 (2011年1月至2021年9月).
- 包括在5岁及以上的个体中使用ML检测昏迷的研究与HUTT监测的血液动力学参数.
- 数据提取包括参与者的人口统计,昏迷协议,ML类型,血液动力学参数和性能指标.
主要成果:
- 涉及1205名参与者 (5-82岁) 的10项研究符合纳入标准.
- 总体ML算法性能:88.8%的灵敏度,81.5%的特异性和85.8%的准确性.
- ML算法需要比传统的评分方法更少的参数来诊断昏迷.
结论:
- 与传统的评分系统相比,机器学习在昏迷诊断方面表现出卓越的性能.
- 整合ML可以减少不必要的住院治疗,提高诊断精度.
- 预计未来使用更大的数据集进行的研究将进一步增强在同步管理中的ML应用.
更多相关视频
08:05Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
10.7K
04:54Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
Published on: November 8, 2024
539
相关概念视频
01:19Quality Assurance
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
01:15Data Validation
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include: