预测哈萨克斯坦对医疗工作者的区域需求:功能主要组件分析方法
Berik Koichubekov1, Bauyrzhan Omarkulov2, Nazgul Omarbekova1
1Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan.
概括
本研究使用功能主要组件分析 (FPCA) 预测哈萨克斯坦.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
- 人口统计学 人口统计学
背景情况:
- 卫生工作人员的分配对于服务提供至关重要.
- 医疗保健专业人员的错误分配是一个国家挑战.
- 预测未来的医疗保健需求对于政策规划至关重要.
研究的目的:
- 应用功能主要组件分析 (FPCA) 来分析哈萨克斯坦卫生工作者分布模式.
- 预测到2033年对医疗人员的需求.
- 确定医疗保健工作人员的区域趋势和增长率.
主要方法:
- 功能主要组件分析 (FPCA) 用于维度减少和模式识别.
- 用扩展窗口的滚动起源交叉验证来评估预测的准确性.
- 预测使用自回归集成移动平均 (ARIMA) 和长短期记忆 (LSTM) 模型进行.
- 长期短期记忆 (LSTM) 与自回归集成移动平均线 (ARIMA) 相比,显示出更高的准确性.
主要成果:
- FPCA成功地确定了医生数量的国家和地区趋势.
- 医疗工作人员的不同区域增长率被确定.
- 预测到2033年每个地区对医生的需求.
- 在预测准确度方面,LSTM模型的表现优于ARIMA.
结论:
- FPCA是分析医疗保健人力资源的宝贵工具.
- 该研究提供了对哈萨克斯坦未来医疗人员需求的近似评估.
- 这些发现可以为卫生工作人员分配和政策制定的战略规划提供信息.
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