Summary of Leveraging Federated Learning For Automatic Detection Of Clopidogrel Treatment Failures, by Samuel Kim and Min Sang Kim
Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures
by Samuel Kim, Min Sang Kim
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 study develops predictive models to optimize patient care by addressing clopidogrel treatment failure detection using federated learning strategies. The authors leverage multiple healthcare institutions to jointly train machine learning models while safeguarding sensitive patient data. They utilize the UK Biobank dataset, partitioned based on geographic centers, and evaluate performance metrics such as Area Under the Curve (AUC) values and convergence rates. Results show that centralized training achieves higher AUC values and faster convergence but federated learning approaches can narrow this performance gap substantially. The study contributes to the growing body of research on federated learning in healthcare, offering a promising avenue for personalized treatment strategies while respecting data privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps doctors give better care to patients by creating models that predict when a common medicine called clopidogrel might not work. To do this, researchers used a special way of training computers together without sharing patient information. They tested this method using a big dataset of people’s health information and found it worked well. This could help doctors make more personalized treatment plans for patients while keeping their data safe. |
Keywords
* Artificial intelligence * Auc * Federated learning * Machine learning
