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

• URL: http://arxiv.org/abs/2407.00840v1
• Date: Sun, 30 Jun 2024 21:54:41 GMT
• Title: MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records
• Authors: Zekai Wang, Tieming Liu, Bing Yao,
• Abstract summary: We propose a Missingness-aware mUlti-branching Self-attention (MUSE-Net) to cope with the challenges in modeling longitudinal EHRs for data-driven disease prediction. We evaluate the proposed MUSE-Net using both synthetic and real-world datasets.
• Score: 11.130065253661147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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