Summary of Federated Random Forest For Partially Overlapping Clinical Data, by Youngjun Park et al.
Federated Random Forest for Partially Overlapping Clinical Data
by Youngjun Park, Cord Eric Schmidt, Benedikt Marcel Batton, Anne-Christin Hauschild
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A comprehensive approach is required to address challenges posed by partially overlapping features and incomplete data in clinical datasets. Federated random forests can achieve promising outcomes when features align, but standard algorithms like random forest require identical parameters across all datasets. To overcome these limitations, this work adapts the federated random forest concept to a setting with partially overlapping features, assessing its effectiveness for partially overlapping clinical data. The approach tackles issues related to the number of involved parties and varying feature overlaps. Evaluation is conducted across three clinical datasets, demonstrating superior performance of the federated random forest model compared to its local counterpart, even in scenarios with imbalanced classes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated random forests are a way to analyze big medical data without sharing it all. Doctors from different hospitals often have different ways of recording patient information, which makes it hard to combine their data. This paper shows how using a special kind of forest (a type of machine learning model) can help combine this data and make better predictions about patients’ health. The researchers tested this approach on three big sets of medical data and found that it worked really well, even when the data was incomplete or had different information. |
Keywords
» Artificial intelligence » Machine learning » Random forest
