Summary of Static and Multivariate-temporal Attentive Fusion Transformer For Readmission Risk Prediction, by Zhe Sun et al.
Static and multivariate-temporal attentive fusion transformer for readmission risk prediction
by Zhe Sun, Runzhi Li, Jing Wang, Gang Chen, Siyu Yan, Lihong Ma
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper proposes a novel approach, called SMTAFormer, for predicting the short-term readmission of ICU patients using both static and multivariate temporal data collected from ICU monitors. The model learns useful feature representations using an MLP network and a temporal transformer network, which are then fused to capture intra-correlation among multivariate temporal features and inter-correlation between static and multivariate temporal features. Experiments on the MIMIC-III dataset show that SMTAFormer outperforms advanced methods, achieving an accuracy of up to 86.6% and an AUC of up to 0.717. This improves the efficiency of resource assignment by assisting physicians in making discharge decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors predict whether a patient will come back to the hospital soon after leaving the intensive care unit (ICU). They use special machines that track patients’ health and combine this information with other details. The new approach, called SMTAFormer, is better than existing methods at predicting when patients might need to return to the ICU. This can help doctors make better decisions about when patients are ready to go home. |
Keywords
» Artificial intelligence » Auc » Transformer
