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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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人工智能驱动的预测和班次优化为儿科急诊室拥挤的人群.

Izzet Turkalp Akbasli1, Ahmet Ziya Birbilen1, Ozlem Teksam1

  • 1Division of Pediatric Emergency, Department of Pediatrics, Faculty of Medicine, Hacettepe University, Ankara 06270, Turkey.

JAMIA open
|March 24, 2025
PubMed
概括

使用机器学习操作 (MLOps) 的人工智能系统准确预测儿科急诊室 (PED) 拥挤情况,并优化医生的时间表,在高峰时段改善患者与医生的比率.

科学领域:

  • 医疗保健中的人工智能
  • 机器学习操作 (MLOps)
  • 医疗保健系统工程 系统工程

背景情况:

  • 儿科急诊室 (PEDs) 面临着影响患者护理和运营效率的拥挤挑战.
  • 预测和人员配置的传统方法往往是反应性的,可能无法适应动态的患者数量.
  • 优化医生的时间表对于管理工作量和确保足够的患者覆盖率至关重要.

研究的目的:

  • 开发和评估一个人工智能驱动的系统,用于预测PED过度拥挤.
  • 使用机器学习操作 (MLOps) 来优化医生轮班时间表.
  • 提高过度拥挤预测的准确性,改善劳动力分布.

主要方法:

  • 从2018年1月到2023年5月,对352,843个PED入学的分析.
  • 开发和比较20个时间序列预测模型,包括深度学习架构.
  • 实施MLOps模拟,用于自动更新数据和重新训练模型.
  • 使用基于预测患者数量的整数线性编程,优化医生班次.

主要成果:

  • 先进的深度学习模型实现了高达75%的R2得分,MLOps将R2中位数从44%提高到60%.
  • 轮班优化调整了84个轮班中的69个轮班的人员配置,增加了在高峰时段的医生分配.
关键词:
儿科急诊部 儿科急诊部人工智能的人工智能是人工智能.预测 预测 预测 预测机器学习操作机器学习操作.过度拥挤 过度拥挤转变优化转变优化时间序列分析分析时间序列分析

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  • 在特定的班次中,人工智能系统将患者与医生的比率平均降低了4.32至4.40个患者.

    • 结论:

    • 人工智能和MLOps集成系统有效地预测PED过度拥挤,并优化医生班次,优于传统方法.
    • 该系统表现出对数据漂移的弹性,并改善了工作人员分布,而不会增加员工人数.
    • 未来的研究应该专注于多中心验证和现实世界的实施,以获得更广泛的影响.