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时间序列预测在印度的精神卫生设施的床位占用,使用机器学习

G Avinash¹, Hariom Pachori², Avinash Sharma³

¹Department of Biostatistics, Division of Health Sciences, ICMR-National Institute of Occupational Health, Ahmedabad, Gujarat, 380016, India.

Scientific reports

|January 21, 2025

概括

这项研究使用六种机器学习模型预测了印度的精神卫生保健病床占用率. 准确的预测有助于医疗保健管理人员在资源配置和能力规划中改善心理健康服务.

科学领域:

医疗保健管理的管理
医疗保健中的人工智能
心理健康服务研究心理健康服务研究

背景情况:

全球和印度不断增加的心理健康问题需要改善医疗保健资源管理.
准确预测医院病床占用率对于优化患者护理和减少医疗机构负担至关重要.
有效的能力规划需要对心理健康服务提供可靠的数据驱动洞察力.

研究的目的:

用机器学习预测印度一家主要精神病院每周的病床占用率.
为此任务评估六种不同的机器学习模型的预测性能.
为医疗保健管理人员提供有关能力规划和资源分配的可操作的见解.

主要方法:

应用六种机器学习模型:支持向量回归,极端梯度提升,随机森林,K-最近邻居,梯度提升和决策树.
利用印度第二大精神病院的2008年至2024年的历史每周床位占用数据.
使用平均绝对百分比误差 (MAPE) 和Diebold-Mariano测试进行性能比较来评估模型的准确性.

主要成果:

该研究成功地预测了每周精神卫生保健病床占用率.
对比分析确定了所使用的机器学习模型的预测性能.
预测为心理健康管理中的战略决策提供了关键数据.

结论:

机器学习模型是预测精神卫生保健病床占用率的有效工具.
准确的占用预测支持心理健康设施的高效能力规划和资源分配.
这些发现有助于通过数据驱动的战略提高印度心理健康服务的质量和可访问性.

关键词:

预测床位占用率的预测医疗保健资源管理医疗保健资源管理机器学习在医疗保健中的应用精神卫生医院精神卫生医院优化患者护理的优化时间序列分析时间序列分析.

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