Summary of Automated Fusion Of Multimodal Electronic Health Records For Better Medical Predictions, by Suhan Cui et al.
Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions
by Suhan Cui, Jiaqi Wang, Yuan Zhong, Han Liu, Ting Wang, Fenglong Ma
First submitted to arxiv on: 20 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 AutoFM neural architecture search (NAS) framework automates the process of designing optimal models for mining Electronic Health Record (EHR) data. Current approaches rely on intuition and empirical experiences, leading to sub-optimal model architectures and limited performance. The framework searches for the best model architectures for encoding diverse input modalities and fusion strategies, outperforming existing state-of-the-art methods and discovering meaningful network architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses deep learning techniques to improve healthcare services by mining medical data from EHR systems. However, designing models that work well with this complex data is challenging due to the many different types of data and how they are structured. The authors propose a new way to design models called AutoFM, which can automatically find the best model architecture for analyzing EHR data. |
Keywords
* Artificial intelligence * Deep learning
