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Updated: May 14, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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全人口抑郁症发病率预测比较自回归集成移动平均线和向量自回归集成移动平均线与时间融合变压器:纵向观测研究

Deliang Yang1, Yiyi Tang1,2, Vivien Kin Yi Chan3

  • 1Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).

Journal of medical Internet research
|May 12, 2025
PubMed
概括

时间融合变压器 (TFT) 在预测抑郁症发病率方面优于ARIMA和VARIMA模型,特别是在公共卫生危机期间. TFT表现出对突然变化的稳定性,为心理健康负担预测提供了更高的准确性.

关键词:
在阿里马,阿里马就是阿里马.深度学习是一种深度学习.抑郁症发病率预测 抑郁症发生率预测电子健康记录是电子健康记录.机器学习是机器学习.医疗信息学是一门医学信息学专业.全人口抑郁症发病率结构性崩的场景时间融合变压器矢量-ARIMA-ARIMA 的时间

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