在巴林王国使用机器学习预测医疗废物产生和相关因素
Khadija Al-Omran1,2, Ezzat Khan3,4
1Environment and Sustainable Development, College of Science, University of Bahrain, Sakhir, 32038, Kingdom of Bahrain. khadija.alomran@polytechnic.bh.
概括
机器学习准确地预测了巴林医院的医疗废物产生. 关键因素包括患者数量,手术和人口,指导有效的废物管理策略.
科学领域:
- 环境健康 环境健康
- 医疗保健管理健康管理
- 数据科学数据科学数据科学
背景情况:
- 有效的医疗废物管理对于公共卫生和环境安全至关重要.
- 公共和私人医疗保健部门都需要强大的废物处理战略.
- 识别影响医疗废物产生因素对于有效规划至关重要.
研究的目的:
- 使用机器学习技术估计医疗废物产生.
- 确定与公共和私人医院医疗废物产生相关的关键因素.
- 评估机器学习模型在预测医疗废物量方面的有效性.
主要方法:
- 在巴林使用了2018-2022 (私人) 和2019-2023 (政府) 的每月医疗废物数据.
- 应用机器学习,特别是集体投票回归器,用于废物产生估计.
- 分析的特征的重要性,以确定医疗废物的重要预测因素.
主要成果:
- 整体投票回归模型实现了高准确性,解释了90.4% (政府) 和91.7% (私人) 的差异.
- 政府医院的重要预测因素:住院患者,人口,手术,门诊.
- 私立医院的重要预测因素:住院患者,分娩,个人收入,手术,门诊.
结论:
- 机器学习模型,特别是集体投票回归器,对于预测医疗废物产生是有效的.
- 了解患者数量和医院服务等影响因素对于有针对性的废物管理至关重要.
- 结果为优化医疗废物规划在各种医疗保健机构提供了宝贵的见解.
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