Summary of Interpretable Prediction and Feature Selection For Survival Analysis, by Mike Van Ness et al.
Interpretable Prediction and Feature Selection for Survival Analysis
by Mike Van Ness, Madeleine Udell
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper presents a new survival analysis model called DyS (pronounced “dice”), which achieves both strong discrimination and interpretability in modeling time-to-event data. The goal is to create a model that doctors can trust and understand by describing how changing each feature impacts the outcome, while only using a small number of features. To achieve this, the authors develop a feature-sparse Generalized Additive Model called DyS, which combines feature selection and interpretable prediction into one model. The paper shows that DyS competes with state-of-the-art machine learning models for survival analysis tasks, particularly in large datasets found in observational healthcare studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DyS is a new approach to predicting patient risk using time-to-event data. It’s like trying to figure out how long it will take for someone to get sick. The goal is to make a model that doctors can understand and trust by showing how different factors, like age or medicine taken, affect the outcome. DyS does this by picking only the most important features and explaining how they work together. This makes it better than other models because it’s easier to understand why someone might get sick. |
Keywords
» Artificial intelligence » Feature selection » Machine learning
