在灾后预测未满足的医疗保健需求:一种机器学习方法
Hyun Jin Han1,2, Hae Sun Suh1,2,3
1Department of Regulatory Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea.
International journal of environmental research and public health
|October 14, 2023
概括
灾难幸存者经常面临未满足的医疗保健需求,影响恢复. 机器学习模型有效地预测这些需求,识别健康状况和位置等关键因素,以更好地管理灾害.
科学领域:
- 公共卫生 公共卫生
- 医疗保健服务研究 医疗服务研究
- 灾难医学 灾难医学
背景情况:
- 未满足的医疗保健需求是灾后的一个重大挑战,加剧了健康差距.
- 有效地预测和管理这些需求对于社区的恢复至关重要.
研究的目的:
- 评估和预测灾难后未满足的医疗保健需求.
- 使用机器学习开发一个准确的预测模型.
- 确定影响未满足医疗保健需求的关键因素.
主要方法:
- 分析2017年关于韩国灾难受害者生活变化长期调查数据.
- 机器学习算法的应用:逻辑回归,C5.0和随机森林.
- 根据安德森的健康行为模型和与灾难相关的因素进行特征选择.
主要成果:
- 1659名参与者的31.5%报告了未满足的医疗保健需求.
- 随机森林模型显示出卓越的性能 (精度,准确性,AUC-ROC,F1得分).
- 关键预测因素包括主观健康状况,与灾难相关的条件和住宅区.
结论:
- 机器学习为灾后医疗管理提供了有价值的工具.
- 识别弱势群体和影响因素对于政策制定至关重要.
- 调查结果强调需要有针对性的干预措施,以解决受灾地区未得到满足的医疗保健需求.
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