Summary of Attention on Personalized Clinical Decision Support System: Federated Learning Approach, by Chu Myaet Thwal et al.
Attention on Personalized Clinical Decision Support System: Federated Learning Approach
by Chu Myaet Thwal, Kyi Thar, Ye Lin Tun, Choong Seon Hong
First submitted to arxiv on: 22 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 In this paper, researchers tackle the challenge of building a smarter healthcare infrastructure by proposing a novel deep learning-based clinical decision support system. The goal is to create a personalized system that can provide accurate solutions for healthcare professionals while protecting patient privacy. To achieve this, they employ federated learning, which allows local neural networks to be trained without exchanging confidential patient data. The proposed scheme uses a sequence-to-sequence model architecture with an attention mechanism, enabling rich clinical data mining. This work aims to deliver evolvable characteristics and assist in medical diagnosing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In short, this paper creates a new way for healthcare professionals to get better information to help diagnose diseases. They use special computer models that can learn from lots of different data without sharing the personal details of patients. This helps keep patient information safe while still giving doctors the best possible answers. The system is designed to be able to change and adapt as new information becomes available, making it a valuable tool for healthcare professionals. |
Keywords
* Artificial intelligence * Attention * Deep learning * Federated learning * Sequence model
