Summary of Predicting Survival Of Hemodialysis Patients Using Federated Learning, by Abhiram Raju et al.
Predicting Survival of Hemodialysis Patients using Federated Learning
by Abhiram Raju, Praneeth Vepakomma
First submitted to arxiv on: 14 Dec 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 This research paper proposes a novel approach to predicting the survival time of hemodialysis patients on waiting lists for kidney transplants using Federated Learning (FL). The current methods require large amounts of sensitive data, which are often siloed and not easily accessible. FL offers a promising solution by enabling local models to be trained without sharing individual datasets, thus improving performance while maintaining data privacy. The study focuses on applying FL to predict survival times for hemodialysis patients using data from NephroPlus, India’s largest private network of dialysis centers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is important because it helps create a more accurate and personalized way to predict the survival time of people who are waiting for a kidney transplant. Right now, doctors use models that need lots of sensitive information, which makes it hard to get the data they need. This new approach uses something called Federated Learning, which can help doctors make better predictions without having to share all the individual patient information. |
Keywords
» Artificial intelligence » Federated learning
