Summary of Opsurv: Orthogonal Polynomials Quadrature Algorithm For Survival Analysis, by Lilian W. Bialokozowicz and Hoang M. Le and Tristan Sylvain et al.
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis
by Lilian W. Bialokozowicz, Hoang M. Le, Tristan Sylvain, Peter A. I. Forsyth, Vineel Nagisetty, Greg Mori
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Functional Analysis (math.FA)
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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 novel machine learning method, Orthogonal Polynomials Quadrature Algorithm for Survival Analysis (OPSurv), is introduced for modeling time-continuous functional outputs in single and competing risks scenarios. OPSurv leverages the initial zero condition of Cumulative Incidence functions and a unique decomposition using orthogonal polynomials to learn functional approximation coefficients. This approach effectively counters overfitting, enhancing model expressiveness and control. The method is validated empirically and theoretically, showcasing its robust performance as an advancement in survival analysis with competing risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of analyzing how long people or things will survive is developed. It’s called OPSurv. This tool can handle different types of risks that might affect the outcome, like two diseases affecting a person at the same time. OPSurv works by breaking down the situation into smaller parts and using special mathematical formulas to make predictions. This makes it more accurate than other methods and helps prevent mistakes. The creators tested OPSurv and showed that it’s a big improvement over what was available before. |
Keywords
* Artificial intelligence * Machine learning * Overfitting
