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Updated: Sep 9, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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向可靠的预测:对临床时间序列数据的贝叶斯式持续学习方法

Cao Zhen, Jeanette Poh Wen Jun, Yang Guo

    IEEE journal of biomedical and health informatics
    |September 1, 2025
    PubMed
    概括

    这项研究引入了持续贝叶斯长期短期记忆 (C-BLSTM),这是一种用于临床时间序列数据的新型深度学习方法. C-BLSTM增强了模型的概括性,在现实世界医疗预测中表现优于现有的方法.

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