Summary of Muse-net: Missingness-aware Multi-branching Self-attention Encoder For Irregular Longitudinal Electronic Health Records, by Zekai Wang et al.
MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records
by Zekai Wang, Tieming Liu, Bing Yao
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep learning architecture is proposed for modeling longitudinal electronic health records (EHRs) to enhance data-driven disease prediction. The MUSE-Net model addresses challenges such as irregularly spaced multi-variate time series, incompleteness, and data imbalance by combining four novel modules: missing value masks for imputation, a multi-task Gaussian process, a time-aware self-attention encoder, and an interpretable multi-head attention mechanism. Experimental results demonstrate that MUSE-Net outperforms existing methods on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to use big amounts of clinical data from electronic health records (EHRs) to make better decisions in hospitals. Right now, it’s hard to work with this type of data because it’s not always complete or evenly spaced. The researchers propose a new model called MUSE-Net that can handle these challenges and do a better job of predicting diseases based on EHRs. |
Keywords
* Artificial intelligence * Deep learning * Encoder * Multi head attention * Multi task * Self attention * Time series
