Summary of Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information, By Fedor Sergeev et al.
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
by Fedor Sergeev, Paola Malsot, Gunnar Rätsch, Vincent Fortuin
First submitted to arxiv on: 18 Jul 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 The proposed approach trains acquirers end-to-end using only the downstream loss, inspired by the maximization of conditional mutual information. This method outperforms random acquisition policies, matching the performance of a model with an unrestrained budget, but does not yet surpass a static acquisition strategy. The paper highlights assumptions and outlines avenues for future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to decide which features to measure in complex data streams. This is important in fields like medicine, where having the right information can help doctors make better decisions. Traditionally, people have used random methods or ones that use all available data, but these approaches can be costly and time-consuming. The new approach uses machine learning to choose the most important features, reducing costs while maintaining performance. While it’s not perfect yet, this method shows promise for real-world applications. |
Keywords
» Artificial intelligence » Machine learning
