  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, the application of EHRs in
data-driven modeling faces challenges such as irregularly spaced multi-variate
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 MUSE-Net leverages a
multi-task Gaussian process (MGP) with missing value masks for data imputation,
a multi-branching architecture to address the data imbalance problem, and a
time-aware self-attention encoder to account for the irregularly spaced time
interval in longitudinal EHRs. 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.
