• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Computational Science and Engineering >Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review
【24h】

Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

机译：基于机器学习的心脏决策支持系统的变量：系统文献综述

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.
机译：现在，由于心脏病的复杂性，临床决策支持系统（CDSS）广泛用于心脏护理。该系统文献综述（SLR）的目的是识别用于构建基于机器学习的临床决策支持系统的最常见的变量和机器学习技术。该SLR采用优选的报告项目进行系统审查和META分析（PRISMA）格式。在530篇论文中，只有21篇论文符合纳入标准。在22个最常见的变量中，年龄，性别，心率，呼吸率，收缩压和医学信息变量。此外，我们的结果表明，简化的急性生理学评分（SAP），顺序器官失效评估（沙发）和急性生理学和慢性健康评估（Apache）是CDSS用于心脏护理的最常见评估尺度。 Logistic回归和支持向量机是CDS中最常用的机器学习技术，以预测死亡率和其他心脏病等败血症，心脏骤停，心力衰竭和脓毒症休克。这些变量和评估工具可用于构建基于机器学习的CDS。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号