MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records
<|reference_start|>MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records: 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.<|reference_end|>
@article{wang2024muse-net:,
    title={MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for
  Irregular Longitudinal Electronic Health Records},
    author={Zekai Wang, Tieming Liu, Bing Yao},
    journal={arXiv preprint arXiv:2407.00840},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00840},
    primaryClass={cs.LG}
}