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相关概念视频

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预测性派遣志愿者第一响应者:算法开发和验证.

Michael Khalemsky1, Anna Khalemsky1, Stephen Lankenau2

  • 1Department of Management, Hadassah Academic College, Jerusalem, Israel.

JMIR mHealth and uHealth
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概括
此摘要是机器生成的。

预测志愿者第一响应者 (VFR) 的行为可以改善紧急调度. 结合事件数据,人口统计数据和过去经验的模型准确预测VFR响应的可能性,优化紧急医疗服务.

关键词:
算法算法是一种算法.在决策过程中做出决定.发送 发送 发送 发送调度算法 调度算法 调度算法发送决定的决定调度预测预测 调度预测紧急情况 紧急情况应急响应应对紧急情况的反应.首先响应者是第一响应者.m健康干预措施 m健康干预措施医疗紧急情况 医疗紧急情况移动健康的移动健康手机电话 手机电话手机电话移动电话应用程序手机应用程序应对者应对者应对者应对者应对者智能手机的智能手机智能手机的智能手机.智能手机应用程序 智能手机应用程序基于智能手机的应用程序志愿者是一个志愿者.

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科学领域:

  • 紧急医疗服务 紧急医疗服务
  • 公共卫生技术 公共卫生技术
  • 在医疗保健中的预测分析.

背景情况:

  • 智能手机应用程序越来越多地派遣志愿者急救人员 (VFR) 提供更快的医疗援助.
  • 视频飞行响应率不确定 (17%-47%),导致仅根据预计到达时间 (ETA) 进行次优发送.
  • 目前的算法可能会在不太可能响应的VFR上浪费资源,从而延迟关键援助.

研究的目的:

  • 通过预测VFR响应行为来增强紧急医疗服务的调度.
  • 开发一种新的方法来预测VFR是否会响应或忽略警报.
  • 改善人类调度员和自主系统的决策.

主要方法:

  • 开发并比较了四种分析模型来预测VFR响应.
  • 模型利用了紧急事件的特征,VFR人口统计数据和以前的经验.
  • 测试了对112个VFR的12个月数据的模型,这些数据响应了188个阿片类药物过量紧急情况.

主要成果:

  • 使用事件数据,人口统计数据和经验的模型在预测被忽视的警报方面取得了高准确性 (高达79.1%).
  • 模型4,包括"频繁响应"指标,显示总体准确度略高 (75.4%).
  • 排除人口统计数据的模型准确地预测了被忽视的警报 (84.2%),但预测的响应警报 (46.5%) 却很差. J48决策树算法表现最好.

结论:

  • 预测VFR响应概率允许优化候选人选择,而不仅仅是ETA.
  • 将响应概率集成到调度算法中,代表了新一代的应急响应.
  • 这些发现有助于VFR网络管理员通过预测分析改善响应时间并挽救生命.