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评估自我注意机制在结核病时间序列预测中的有效性

Zhihong Lv¹, Rui Sun¹, Xin Liu¹

¹Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, 410013, China.

BMC infectious diseases

|December 3, 2024

概括

准确的结核病例预测对于公共卫生至关重要. 与LSTM和ARIMA相比,一种新的自我注意模型显著提高了预测准确度,有助于卫生资源分配.

科学领域:

流行病学流行病学
机器学习机器学习
公共卫生公共卫生

背景情况:

结核病 (TB) 构成了重大的公共卫生挑战,需要准确的预测,以有效管理资源.
优化卫生资源和医疗服务分配需要精确预测未来的结核病 (TB) 病例.

研究的目的:

评估自我注意机制在预测结核病例数量的有效性.
将自我注意力模型的预测性能与LSTM和ARIMA等既有模型进行比较.

主要方法:

采用了长德市 (2010-2021) 每月结核病例数据.
我们构建了一个自我注意模型,一个长期短期记忆 (LSTM) 模型和一个自行回归集成移动平均 (ARIMA) 模型.
模型性能使用根平均平方误差 (RMSE),平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE) 进行评估.

主要成果:

自我注意模型在LSTM和ARIMA模型中显示出更高的预测准确性.
与LSTM相比,自我注意力模型实现了较低的RMSE (约. 7.41%),MAE (大约为一个人). 10.99%),以及MAPE (大约为10.99%) 和MAPE (大约为10.99%). 9.87%). 这是一个很好的例子.
与ARIMA相比,观察到显著的改善,RMSE减少 (约. 28.86%),MAE (大约为一个人). 32.22%) 和MAPE (大约约. 29.89%) 的比例.

结论:

自我注意模型为改善结核病例预测准确度提供了一种高度有效的方法.
调查结果为卫生部门提供了宝贵的指导,以优化对结核病控制的资源分配和医疗服务.

关键词:

在ARIMA模型模型.在 LSTM 模型中.自我注意力机制机制时间序列预测时间序列预测结核病是一种疾病.

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