MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification

• URL: http://arxiv.org/abs/2502.04515v1
• Date: Thu, 06 Feb 2025 21:34:54 GMT
• Title: MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification
• Authors: Wei Fan, Jingru Fei, Dingyu Guo, Kun Yi, Xiaozhuang Song, Haolong Xiang, Hangting Ye, Min Li,
• Abstract summary: We propose a Multi-resolution Spatio Graph Learning framework, MedGNN, for medical time series classification. We first propose to construct multi-resolution adaptive graph structures to learn dynamic multi-scale embeddings. We then propose Difference Attention Networks to operate self-attention mechanisms on the finite difference for temporal modeling.
• Score: 9.290150386783838
• License:
• Abstract: Medical time series has been playing a vital role in real-world healthcare systems as valuable information in monitoring health conditions of patients. Accurate classification for medical time series, e.g., Electrocardiography (ECG) signals, can help for early detection and diagnosis. Traditional methods towards medical time series classification rely on handcrafted feature extraction and statistical methods; with the recent advancement of artificial intelligence, the machine learning and deep learning methods have become more popular. However, existing methods often fail to fully model the complex spatial dynamics under different scales, which ignore the dynamic multi-resolution spatial and temporal joint inter-dependencies. Moreover, they are less likely to consider the special baseline wander problem as well as the multi-view characteristics of medical time series, which largely hinders their prediction performance. To address these limitations, we propose a Multi-resolution Spatiotemporal Graph Learning framework, MedGNN, for medical time series classification. Specifically, we first propose to construct multi-resolution adaptive graph structures to learn dynamic multi-scale embeddings. Then, to address the baseline wander problem, we propose Difference Attention Networks to operate self-attention mechanisms on the finite difference for temporal modeling. Moreover, to learn the multi-view characteristics, we utilize the Frequency Convolution Networks to capture complementary information of medical time series from the frequency domain. In addition, we introduce the Multi-resolution Graph Transformer architecture to model the dynamic dependencies and fuse the information from different resolutions. Finally, we have conducted extensive experiments on multiple medical real-world datasets that demonstrate the superior performance of our method. Our Code is available.

Related papers

• General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data [61.163542597764796]
  We show that time series with different time granularities (or corresponding frequency resolutions) exhibit distinct joint distributions in the frequency domain. A novel Fourier knowledge attention mechanism is proposed to enable learning time-aware representations from both the temporal and frequency domains. An autoregressive blank infilling pre-training framework is incorporated to time series analysis for the first time, leading to a generative tasks agnostic pre-training strategy.
  arXiv  Detail & Related papers  (2025-02-05T15:20:04Z)
• MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis [6.30440420617113]
  We introduce MedTsLLM, a general multimodal large language model (LLM) framework that integrates time series data and rich contextual information in the form of text to analyze physiological signals. We perform three tasks with clinical relevance: semantic segmentation, boundary detection, and anomaly detection in time series. Our model outperforms state-of-the-art baselines, including deep learning models, other LLMs, and clinical methods across multiple medical domains.
  arXiv  Detail & Related papers  (2024-08-14T18:57:05Z)
• Automated Ensemble Multimodal Machine Learning for Healthcare [52.500923923797835]
  We introduce a multimodal framework, AutoPrognosis-M, that enables the integration of structured clinical (tabular) data and medical imaging using automated machine learning. AutoPrognosis-M incorporates 17 imaging models, including convolutional neural networks and vision transformers, and three distinct multimodal fusion strategies.
  arXiv  Detail & Related papers  (2024-07-25T17:46:38Z)
• Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training [99.2891802841936]
  We introduce the Med-ST framework for fine-grained spatial and temporal modeling. For spatial modeling, Med-ST employs the Mixture of View Expert (MoVE) architecture to integrate different visual features from both frontal and lateral views. For temporal modeling, we propose a novel cross-modal bidirectional cycle consistency objective by forward mapping classification (FMC) and reverse mapping regression (RMR)
  arXiv  Detail & Related papers  (2024-05-30T03:15:09Z)
• Temporal Cross-Attention for Dynamic Embedding and Tokenization of Multimodal Electronic Health Records [1.6609516435725236]
  We introduce a dynamic embedding and tokenization framework for precise representation of multimodal clinical time series. Our framework outperformed baseline approaches on the task of predicting the occurrence of nine postoperative complications.
  arXiv  Detail & Related papers  (2024-03-06T19:46:44Z)
• Coupled Attention Networks for Multivariate Time Series Anomaly Detection [10.620044922371177]
  We propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data. To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module.
  arXiv  Detail & Related papers  (2023-06-12T13:42:56Z)
• An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning [14.353798949041698]
  We propose an adaptive federated relevance framework, namely FedRel, for spatial-temporal graph learning. The core Dynamic Inter-Intra Graph (DIIG) module in the framework is able to use these features to generate the spatial-temporal graphs. To improve the model generalization ability and performance while preserving the local data privacy, we also design a relevance-driven federated learning module.
  arXiv  Detail & Related papers  (2022-06-07T16:12:17Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)
• A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders [1.9659095632676098]
  This paper proposes a new approach to network development named GraphTS to overcome the limited accuracy of existing methods. For this purpose, EMG signals are pre-processed and mapped to a complex network by a standard visibility graph algorithm. The resulting networks can differentiate between healthy and patient samples.
  arXiv  Detail & Related papers  (2021-08-16T06:44:48Z)
• Cross-Modal Information Maximization for Medical Imaging: CMIM [62.28852442561818]
  In hospitals, data are siloed to specific information systems that make the same information available under different modalities. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time. We propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time.
  arXiv  Detail & Related papers  (2020-10-20T20:05:35Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.