使用流行病学监督和无监督机器学习分析评估中东地区救护车碰撞审查小组的结果
Hassan Farhat1,2,3, Guillaume Alinier1,4,5,6, Rafik Khedhiri1
1Ambulance Service, Hamad Medical Corporation, PO Box 3050, Doha, Qatar.
Qatar medical journal
|August 27, 2025
概括
救护车碰撞仍然是一个风险,但使用机器学习 (ML) 的专门审查小组可以识别模式并预测未来的趋势以提高安全性. 这项研究分析了131起事件,以制定风险管理策略.
科学领域:
- 职业健康和安全
- 运输安全
- 在医疗保健中的数据科学
背景情况:
- 救护车碰撞对工作人员,患者和公众造成重大职业风险.
- 尽管采取了安全措施,但应急响应的复杂性挑战了碰撞风险的降低.
- 了解和管理这些风险对于医疗保健服务至关重要.
研究的目的:
- 调查哈马德医疗公司 (HMCAS) 专门的车辆碰撞审查小组的作用.
- 识别,理解和管理与救护车碰撞相关的风险.
- 用数据分析来改善救护车的安全协议.
主要方法:
- 从2023年起对131辆HMCAS救护车碰撞记录进行了回顾性定量分析.
- 使用描述性,双变量和机器学习 (ML) 技术:多项逻辑回归 (MLR),决策树 (DT),关联规则挖掘 (ARM) 和时间序列预测.
- 使用机器学习来发现隐藏的模式,预测洞察力和未来的碰撞趋势.
主要成果:
- 大多数分析事件涉及城市救护车.
- MLR和DT模型的预测准确率分别为41%和35%.
- 时间序列预测预测事件的逐渐增加,然后稳定.
结论:
- 专门的碰撞审查小组对于管理和减轻救护车碰撞风险至关重要.
- 机器学习技术为安全管理中的知情决策提供基于证据的支持.
- 进一步的研究应该评估有针对性的培训和安全协议的长期影响.
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