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结合机器学习和优化,用于操作患者床位分配问题.

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概括
此摘要是机器生成的。

医院可以通过使用机器学习 (ML) 来预测紧急到来来改善患者床位分配. 这种方法提高了超过17%的准确性,优化了资源配置和患者护理.

关键词:
紧急情况预测预测.紧急病人的入院情况医院病床管理管理机器学习 机器学习运营管理是运营管理.运营研究 运营研究患者病房的分配任务利益相关者的整合 利益相关者的整合

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科学领域:

  • 运营研究 运营研究
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 由于不可预测的紧急病人的到来,医院床位分配是复杂的.
  • 有限的病床容量需要准确的预测来管理患者流量并避免短缺.
  • 目前的方法与紧急入院固有的不确定性作斗争.

研究的目的:

  • 用机器学习 (ML) 开发一种改进的患者床位分配模型,用于紧急患者预测.
  • 通过整合多种数据来源,提高预测紧急住院患者到达的准确性.
  • 为现实世界患者床位分配问题创建高级优化启发式.

主要方法:

  • 实施的ML模型包括天气,时间,事件和占用数据,以预测紧急病人的到来.
  • 开发了一种新的超启发式,将试点方法与贪的前 (GLA) 启发式相结合,用于优化床位分配.
  • 根据基线平均值验证了ML预测,实现高达17%的更好的RMSE.
  • 将超启发式与现有方法进行比较,包括遗传算法.

主要成果:

  • 与传统方法相比,ML预测显著提高了紧急住院患者到达的准确性.
  • 开发的超启发式表现出了卓越的性能,比基准标准提高了多达5.3%的目标功能.
  • 结合了ML预测和超启发性优化,在现实世界问题上取得了3.3%的改进.

结论:

  • 机器学习提供了一个强大的工具,可以提高紧急病人的到达预测的准确性.
  • 先进的优化启发式,就像拟议的超启发式一样,对于高效的医院床位管理至关重要.
  • 将预测性ML与优化技术相结合,可以大大提高医院运营效率和患者护理.