贝叶斯模型平均基于深度学习预测精神卫生机构的住院病床占用率
1Division of Health Sciences, Department of Biostatistics, ICMR-National Institute of Occupational Health, Ahmedabad, Gujarat, 380016, India.
Scientific reports
|November 3, 2025
概括
这项研究引入了一种新的预测框架,使用贝叶斯模型平均 (BMA) 和深度学习来预测印度的精神卫生医院病床占用率. BMA-GS模型实现了98.06%的准确性,改善了资源分配和患者护理.
科学领域:
- 医疗保健管理的管理
- 人工智能的人工智能
- 心理健康服务 心理健康服务
背景情况:
- 心理健康障碍影响全球15%以上的劳动年龄人口,造成重大经济损失.
- 印度面临着巨大的心理健康治疗缺口,由流行病后的激增和紧张的基础设施加剧.
- 预测不足导致医院过度拥挤和超出容量,需要用于规划的预测工具.
研究的目的:
- 开发和评估一种新的预测框架,用于预测每周的精神健康医院病床占用率.
- 将贝叶斯模型平均化 (BMA) 与泽尔纳的g-prior和深度学习模型集成,以提高预测准确度.
- 支持印度医院管理人员和政策制定者的数据驱动决策.
主要方法:
- 分析了来自印度第二大精神卫生医院的2008-2024年时间序列数据.
- 6个深度学习模型 (TDNN,RNN,GRU,LSTM,BiLSTM,BiGRU) 通过随机搜索 (RS) 和网格搜索 (GS) 进行训练和优化.
- 一个贝叶斯模型平均化 (BMA) 框架,结合Zellner的g-prior,被应用于集合模型预测.
主要成果:
- 双向长短期内存 (BiLSTM) 模型与网格搜索 (GS) 调整以及BMA-GS模型显示出优异的预测性能.
- 该BMA-GS模型实现了98.06%的准确性,平均绝对百分比误差 (MAPE) 为1.939%,预测每周的波动在±13床.
- 与随机搜索相比,网格搜索优化带来了更好的预测精度 (平均可信区间宽度从16.34降至13.28) 和可靠性.
结论:
- 将贝叶斯统计 (BMA与Zellner的g-prior) 集成到深度学习架构中,为医院床位占用率预测提供了一个强大的解决方案.
- 拟议的框架提高了预测的准确性和可靠性,有助于在精神卫生保健机构有效地分配和规划资源.
- 这项研究支持印度国家心理健康计划 (NMHP) 和可持续发展目标3,通过促进公平有效的心理健康护理.
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