利用机器学习和地理分析来改善事故后交通伤害管理和应急响应系统
1Division of Forensic Medicine, Maharat Nakhon Ratchasima Hospital, Amphure Muang, Thailand.
International journal of injury control and safety promotion
|April 11, 2025
概括
机器学习和地理分析确定了泰国的高风险交通伤害区域. 这有助于改善紧急医疗服务 (EMS) 和交通安全规划,以更好地应对道路事故.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 道路交通伤害是全球重大公共卫生挑战.
- 有效的交通安全策略需要精确识别高风险区域,并有效应对紧急情况.
- 泰国纳孔拉查西马省面临着大量的道路交通死亡事故,需要改进管理.
研究的目的:
- 用机器学习 (ML) 和地理分析分析道路交通死亡人数.
- 确定交通伤害高风险区域,并优化紧急医疗服务 (EMS) 的位置.
- 为了评估预测受伤地点最近的创伤中心的准确性.
主要方法:
- 从法医和医院记录收集了有关道路交通事故死亡的数据.
- 雇佣K-意味着集群到死亡地点,并确定高风险集群.
- 利用球树算法和谷歌方向API用于最近的创伤中心识别.
- 进行统计测试 (基平方,克鲁斯卡尔-瓦利斯) 来分析人口相关性.
主要成果:
- 对181例病例的分析显示,患者的平均年龄为37岁,男性占83.43%.
- 确定了四个不同的高风险集群,具有高的轮得分 (0.94),表明适当的EMS位置.
- 预测最近的医院实现了高性能指标 (准确性,精度,回忆,F1得分为0.90).
结论:
- 机器学习和地理分析是加强交通伤害管理的强大工具.
- 调查结果支持有针对性的干预和资源分配,以改善交通安全和应急响应.
- 该研究表明,数据驱动的方法有潜力减少道路交通死亡人数.
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