Summary of Mpre: Multi-perspective Patient Representation Extractor For Disease Prediction, by Ziyue Yu et al.
MPRE: Multi-perspective Patient Representation Extractor for Disease Prediction
by Ziyue Yu, Jiayi Wang, Wuman Luo, Rita Tse, Giovanni Pau
First submitted to arxiv on: 1 Jan 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 proposed Multi-perspective Patient Representation Extractor (MPRE) is a novel approach for disease prediction based on electronic health records (EHR). By leveraging Frequency Transformation Module (FTM), 2D Multi-Extraction Network (2D MEN), and First-Order Difference Attention Mechanism (FODAM), MPRE aims to extract trends, variations, and correlations in dynamic features. This improves model performance by addressing the limitations of sparse visit records and existing works. MPRE outperforms state-of-the-art baseline methods on two real-world public datasets, achieving superior AUROC and AUPRC scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MPRE is a new way to help doctors predict diseases using patient data from electronic health records (EHRs). The team developed special tools like FTM, 2D MEN, and FODAM to extract important trends and patterns from the data. This makes predictions more accurate and helps address limitations of existing methods. By testing MPRE on real-world datasets, researchers showed that it outperforms other approaches in detecting diseases. |
Keywords
* Artificial intelligence * Attention
