Frontiers in public health · 2026
Dynamic temporal partitioning enhanced transformer for pediatric viral load forecasting.
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Li Sai, Li Zhengqiu, Mo Yi, Chen Sitian, Ning Zeshu, Ren Junkai, He Xiaozhou
Аннотация
INTRODUCTION: Viral infectious diseases are highly prevalent in pediatric populations, and accurate prediction of viral load is critical for clinical intervention and public health management. Existing time-series models poorly handle long-range dependencies, multi-scale features, noise, and incomplete clinical data.
METHODS: We propose DTR-Former, a Dynamic Temporal Partitioning-enhanced Transformer. It uses wavelet packet decomposition for adaptive temporal partitioning and multi-scale feature extraction, adopts sparse self-attention to reduce redundancy and enhance long-sequence modeling, and employs a residual convolutional decoder with a gating mechanism to refine features and suppress residual noise.
RESULTS: On dbEBV dataset, DTR-Former achieves MSE = 0.16, MAE = 0.27, R 2 = 0.88; on NCBI dataset, MSE = 0.18, MAE = 0.30, R 2 = 0.86. It shows strong multi-step stability and robustness under missing-data conditions.
CONCLUSION: DTR-Former outperforms state-of-the-art methods in accuracy, stability, and efficiency, offering an effective solution for pediatric viral load forecasting and related time-series tasks.
METHODS: We propose DTR-Former, a Dynamic Temporal Partitioning-enhanced Transformer. It uses wavelet packet decomposition for adaptive temporal partitioning and multi-scale feature extraction, adopts sparse self-attention to reduce redundancy and enhance long-sequence modeling, and employs a residual convolutional decoder with a gating mechanism to refine features and suppress residual noise.
RESULTS: On dbEBV dataset, DTR-Former achieves MSE = 0.16, MAE = 0.27, R 2 = 0.88; on NCBI dataset, MSE = 0.18, MAE = 0.30, R 2 = 0.86. It shows strong multi-step stability and robustness under missing-data conditions.
CONCLUSION: DTR-Former outperforms state-of-the-art methods in accuracy, stability, and efficiency, offering an effective solution for pediatric viral load forecasting and related time-series tasks.