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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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DynaMamba: Multi-scale dynamic interacting Mamba network for irregular clinical time series classification.

Hao Chen1, Junjie Zhang1, Xiaowei Yan1

  • 1School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.

Journal of Biomedical Informatics
|March 25, 2026
PubMed
Summary
This summary is machine-generated.

DynaMamba, a new network for irregular clinical time series, improves disease diagnosis and mortality prediction. It effectively models complex patient data by capturing multi-scale variations and inter-variable dependencies.

Keywords:
Clinical time series classificationDeep learningMamba

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Data Science

Background:

  • Irregular clinical time series data are crucial for patient care but challenging to model.
  • Existing methods struggle with multi-scale temporal variations, irregular sampling, and inter-variable dependencies.

Purpose of the Study:

  • To introduce DynaMamba, a novel network designed to effectively model irregular clinical time series.
  • To address the challenges of multi-scale temporal variations, irregular sampling, and inter-variable dependencies.

Main Methods:

  • Developed DynaMamba, a multi-scale dynamic interacting Mamba network.
  • Implemented a multi-view extraction mechanism (observation, missingness, temporal interval).
  • Utilized a hierarchical multi-scale embedding and dynamic multi-sequence modeling with bidirectional Mamba blocks.

Main Results:

  • DynaMamba achieved state-of-the-art performance on three real-world clinical datasets.
  • Demonstrated superior effectiveness and robustness in handling irregular clinical time series.
  • Outperformed existing methods in disease diagnosis and mortality prediction tasks.

Conclusions:

  • DynaMamba offers a robust solution for modeling complex irregular clinical time series.
  • The proposed innovations effectively capture critical clinical monitoring patterns and dependencies.
  • This network advances the potential for improved clinical decision-making using patient data.