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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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在智能健康中使用机器学习进行基于异常的威胁检测.

Muntaha Tabassum1, Saba Mahmood2, Amal Bukhari3

  • 1Department of Computer Science, Bahria University, Islamabad, Pakistan.

BMC medical informatics and decision making
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概括

这项研究通过使用无监督机器学习来检测异常来提高电子健康记录的安全性. 隔离森林SVM证明最有效,准确识别数据异常,错误阳性较少.

关键词:
异常检测检测异常检测电子健康记录 (EHR) 是一种电子医疗记录.医疗保健 医疗保健 医疗保健 医疗保健内部威胁 内部威胁机器学习 机器学习

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科学领域:

  • 计算机科学 计算机科学
  • 医疗保健信息学 医疗保健信息学

背景情况:

  • 由于数据复杂性和内部威胁的增加,异常检测在医疗保健中至关重要.
  • 电子健康记录 (EHR) 容易受到操纵,需要采取强有力的安全措施.

研究的目的:

  • 通过无监督机器学习提出和评估一种方法来保护EHR免受数据异常的影响.
  • 提高医疗保健数据中的异常检测的准确性和减少虚假阳性.

主要方法:

  • 采用系统方法,包括数据预处理,标签和建模.
  • 使用无监督机器学习算法:隔离森林 (IForest) 和局部异常因素 (LOF) 具有变化 (SVM,决策树,随机森林).
  • 评估模型使用准确度,灵敏度,特异性,F1分数,轮分数和Dunn分数等指标.

主要成果:

  • 隔离森林SVM实现了最高的准确性 (99.21%),灵敏度 (99.75%),特异性 (99.32%) 和F1评分 (98.72%).
  • 隔离森林决策树也表现出强的表现,准确率为98.92%,F1评分为99.35%.
  • 与其他模型相比,隔离森林随机森林的特异性较低 (72.84%).

结论:

  • 隔离森林SVM是EHR异常检测的最高性能模型,提供卓越的准确性和减少错误阳性.
  • 拟议的方法有效地保护敏感的医疗保健数据免受不断变化的数字威胁.
  • 该方法成功地发现了基线方法遗漏的新型上下文异常.