Summary of Addressing Data Heterogeneity in Federated Learning Of Cox Proportional Hazards Models, by Navid Seidi et al.
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models
by Navid Seidi, Satyaki Roy, Sajal K. Das, Ardhendu Tripathy
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP); Machine Learning (stat.ML)
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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 proposes a Federated Learning (FL) approach to personalized survival analysis, tackling challenges in disease profiles and therapeutic approaches across hospitals and health professionals. The study focuses on the Cox Proportional Hazards (CoxPH) model, addressing data heterogeneity and improving model performance through feature-based clustering. Synthetic datasets and real-world applications, including the SEER database, are used to demonstrate the efficacy of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In healthcare, there’s a need for patient-centric personalized strategies. This research uses Federated Learning (FL) to predict survival analysis. They focus on the Cox Proportional Hazards (CoxPH) model and try to make it better by dealing with different data types. They test this approach using fake data and real data from patients. |
Keywords
* Artificial intelligence * Clustering * Federated learning
