MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health...

The era of big data has made vast amounts of clinical data readily available, particularly in the form of electronic health records (EHRs), which provides unprecedented opportunities for developing data-driven diagnostic tools to enhance clinical decision making. However, data-driven modeling of EHRs faces challenges such as irregularly spaced time series, issues of incompleteness, and data imbalance. Realizing the full data potential of EHRs hinges on the development of advanced analytical models. In this paper, we propose a novel Missingness-aware mUlti-branching Self-Attention Encoder (MUSE-Net) to cope with the challenges in modeling longitudinal EHRs for data-driven disease prediction. The proposed MUSE-Net is composed by four novel modules including: (1) a multi-task Gaussian process (MGP) with missing value masks for data imputation; (2) a multi-branching architecture to address the data imbalance problem; (3) a time-aware self-attention encoder to account for the irregularly spaced time interval in longitudinal EHRs; (4) interpretable multi-head attention mechanism that provides insights into the importance of different time points in disease prediction, allowing clinicians to trace model decisions. We evaluate the proposed MUSE-Net using both synthetic and real-world datasets. Experimental results show that our MUSE-Net outperforms existing methods that are widely used to investigate longitudinal signals.

This article is motivated by the growing need for robust machine learning models capable of handling the complexities of real-world EHRs, including irregular time intervals, missing data, and class imbalance. The proposed MUSE-Net model integrates advanced imputation via MGP with missingness masks, a time-aware self-attention encoder, and a multi-branching framework to enhance predictive robustness. Additionally, MUSE-Net leverages an interpretable multi-head attention mechanism to provide transparent decision-making, allowing clinicians to trace model predictions back to key time points. This framework offers a practical and trustworthy solution for disease prediction and clinical decision support.