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DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification

Tao Xie, Zexi Tan, Haoyi Xiao, Binbin Sun, Yiqun Zhang

TL;DR

DE3S tackles the medical ETSC challenge by jointly enhancing weak early signals and balancing local subject-specific and global temporal information. It introduces a Dual Enhancement Mechanism, Attention-based Patch Sparsification, and a Dual-Path MoE-Inception Fusion to create compact yet richly informative patch representations. Empirical results across six real-world datasets show state-of-the-art performance in early prediction and subject-consistency tasks, with ablations confirming the necessity of each component. The approach offers a scalable, efficient framework for timely and accurate medical risk screening, with promising avenues for multi-modal and explainability extensions.

Abstract

Early Time Series Classification (ETSC) is critical in time-sensitive medical applications such as sepsis, yet it presents an inherent trade-off between accuracy and earliness. This trade-off arises from two core challenges: 1) models should effectively model inherently weak and noisy early-stage snippets, and 2) they should resolve the complex, dual requirement of simultaneously capturing local, subject-specific variations and overarching global temporal patterns. Existing methods struggle to overcome these underlying challenges, often forcing a severe compromise: sacrificing accuracy to achieve earliness, or vice-versa. We propose \textbf{DE3S}, a \textbf{D}ual-\textbf{E}nhanced \textbf{S}oft-\textbf{S}parse \textbf{S}equence Learning framework, which systematically solves these challenges. A dual enhancement mechanism is proposed to enhance the modeling of weak, early signals. Then, an attention-based patch module is introduced to preserve discriminative information while reducing noise and complexity. A dual-path fusion architecture is designed, using a sparse mixture of experts to model local, subject-specific variations. A multi-scale inception module is also employed to capture global dependencies. Experiments on six real-world medical datasets show the competitive performance of DE3S, particularly in early prediction windows. Ablation studies confirm the effectiveness of each component in addressing its targeted challenge. The source code is available \href{https://github.com/kuxit/DE3S}{\textbf{here}}.

DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification

TL;DR

DE3S tackles the medical ETSC challenge by jointly enhancing weak early signals and balancing local subject-specific and global temporal information. It introduces a Dual Enhancement Mechanism, Attention-based Patch Sparsification, and a Dual-Path MoE-Inception Fusion to create compact yet richly informative patch representations. Empirical results across six real-world datasets show state-of-the-art performance in early prediction and subject-consistency tasks, with ablations confirming the necessity of each component. The approach offers a scalable, efficient framework for timely and accurate medical risk screening, with promising avenues for multi-modal and explainability extensions.

Abstract

Early Time Series Classification (ETSC) is critical in time-sensitive medical applications such as sepsis, yet it presents an inherent trade-off between accuracy and earliness. This trade-off arises from two core challenges: 1) models should effectively model inherently weak and noisy early-stage snippets, and 2) they should resolve the complex, dual requirement of simultaneously capturing local, subject-specific variations and overarching global temporal patterns. Existing methods struggle to overcome these underlying challenges, often forcing a severe compromise: sacrificing accuracy to achieve earliness, or vice-versa. We propose \textbf{DE3S}, a \textbf{D}ual-\textbf{E}nhanced \textbf{S}oft-\textbf{S}parse \textbf{S}equence Learning framework, which systematically solves these challenges. A dual enhancement mechanism is proposed to enhance the modeling of weak, early signals. Then, an attention-based patch module is introduced to preserve discriminative information while reducing noise and complexity. A dual-path fusion architecture is designed, using a sparse mixture of experts to model local, subject-specific variations. A multi-scale inception module is also employed to capture global dependencies. Experiments on six real-world medical datasets show the competitive performance of DE3S, particularly in early prediction windows. Ablation studies confirm the effectiveness of each component in addressing its targeted challenge. The source code is available \href{https://github.com/kuxit/DE3S}{\textbf{here}}.
Paper Structure (18 sections, 10 equations, 2 figures, 3 tables)

This paper contains 18 sections, 10 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Proposed Method
  3. Overall Architecture
  4. Dual Enhancement Mechanism
  5. Attention-Based Patch Sparsification
  6. Dual-Path MoE-Inception Fusion
  7. Experiments
  8. Experimental Setup
  9. Datasets
  10. Baselines
  11. Prediction Performance Comparison
  12. Early Prediction Results
  13. Subject-Consistency Prediction Results
  14. Ablation Study
  15. Related Work
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Overall framework of DE3S. The architecture implements our core methodology: (1) A Dual Enhancement Mechanism first applies data augmentation and then uses patch embedding and multi-head attention for model-level enhancement. (2) An Attention-based Patch Sparsification module selects and aggregates patch tokens. (3) A Dual-Path Fusion module processes the tokens for final classification.
  • Figure 2: Performance comparison (Accuracy) on the Sepsis1, Sepsis2, and SCC datasets across different temporal lengths. The x-axis represents the observation window, demonstrating the models' early detection capabilities.