Evidence map›Paper›PMID 42326934›Full record

ArticleFrontiers in public health2026

Dynamic temporal partitioning enhanced transformer for pediatric viral load forecasting.

Sai Li, Zhengqiu Li, Yi Mo, Sitian Chen, Zeshu Ning, Junkai Ren, Xiaozhou He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Sai LiDepartment of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, China.
Zhengqiu LiDepartment of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, China.
Yi MoDepartment of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, China.
Sitian ChenDepartment of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, China.
Zeshu NingDepartment of Neurology, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, China.
Junkai RenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiaozhou HeNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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, 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.

Indexed as

Viral LoadVirus DiseasesForecastingHumansdynamic temporal partitioningpediatric infectious disease monitoringpediatric viral load predictionsparse self-attentiontime series analysistransformer

Identifiers

PMID42326934
PMCPMC13279670

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.