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监督学习算法在出院状态预测中对外伤患者进行比较:经验评估

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随机森林算法最好地预测创伤患者的出院状态,改善医院绩效指标. 数据平衡进一步提高预测准确性,以获得更好的患者结果.

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人工智能的人工智能监督机器学习的监督机器学习创伤是一个创伤.创伤中心的创伤中心

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科学领域:

  • 医疗信息学 医疗信息学
  • 在医疗保健中的数据科学.
  • 创伤护理研究 创伤护理研究

背景情况:

  • 创伤中心数据分析确定了影响医院预算的关键绩效指标.
  • 改进的绩效指标可以导致降低死亡率和改善患者健康结果.
  • 在创伤护理中识别成长机会对于医院的财务健康至关重要.

研究的目的:

  • 确定最佳的监督机器学习算法,用于预测创伤患者的出院状态.
  • 在临床环境中评估各种算法的有效性.

主要方法:

  • 使用卡珊创伤登记处数据进行的回顾性研究 (2018年3月 - 2019年2月).
  • 监督算法的评估:天真贝叶斯,物流回归,支持向量机,随机森林和K-最近邻居.
  • 使用准确性,精度,回忆和F测量指标的性能评估,使用持久验证技术.

主要成果:

  • 与其他评估的算法相比,随机森林算法表现出卓越的性能.
  • 随机森林使用信息获取实现了最高的准确度 (84.6%),精度 (79.6%),回忆 (76.8%) 和F-测量 (76.20%).
  • 具体的绩效指标根据所选择的指标 (吉尼指数与信息获取) 略有变化.

结论:

  • 监督算法,当适当配置时,可以有效地帮助诊断创伤患者的出院状态.
  • 在这种情况下,数据平衡技术显示了提高算法性能的潜力.
  • 结果的概括性有限,取决于算法选择和参数调整.