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Updated: Sep 20, 2025

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CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning From Sporadic Temporal

Mostafa Mehdipour Ghazi, Lauge Sorensen, Sebastien Ourselin

    IEEE Transactions on Neural Networks and Learning Systems
    |June 6, 2022
    PubMed
    Summary

    This study introduces CARRNN, a novel deep learning model for analyzing irregular, sporadic data. CARRNN effectively models temporal patterns in healthcare and activity recognition tasks, outperforming existing methods.

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    Area of Science:

    • Machine Learning
    • Deep Learning
    • Time Series Analysis

    Background:

    • Learning temporal patterns from multivariate longitudinal data is challenging due to data irregularity and asynchronicity.
    • Existing deep learning models struggle with sporadic data common in healthcare, lacking adaptability to varying time intervals.
    • This limitation hinders accurate modeling for applications like disease progression and mortality prediction.

    Purpose of the Study:

    • To develop a novel deep learning model capable of modeling temporal features in sporadic, irregular, and asynchronous data.
    • To address the limitations of existing models in handling time-series data with variable time lags.
    • To improve predictive performance in diverse applications including healthcare and activity recognition.

    Main Methods:

    • Introduced CARRNN, an integrated deep learning architecture combining a recurrent neural network (RNN) unit and a continuous-time autoregressive (CAR) model.
    • Employed a generalized discrete-time autoregressive (AR) model, trainable end-to-end with neural networks modulated by time lags.
    • Utilized a gated recurrent unit (GRU) within the CARRNN architecture.

    Main Results:

    • CARRNN demonstrated significantly better predictive performance across multiple time-series regression and classification tasks.
    • The model achieved superior results in Alzheimer's disease progression modeling and ICU mortality rate prediction.
    • Outperformed state-of-the-art methods including RNNs, GRUs, and LSTMs in human activity and event-based digit recognition.

    Conclusions:

    • The proposed CARRNN model effectively handles the challenges of sporadic, irregular, and asynchronous multivariate longitudinal data.
    • CARRNN offers a robust solution for temporal pattern learning in diverse real-world applications, particularly in healthcare.
    • The model's architecture provides a significant advancement over existing deep learning techniques for time-series analysis.