Summary of Sample Selection Bias in Machine Learning For Healthcare, by Vinod Kumar Chauhan et al.
Sample Selection Bias in Machine Learning for Healthcare
by Vinod Kumar Chauhan, Lei Clifton, Achille Salaün, Huiqi Yvonne Lu, Kim Branson, Patrick Schwab, Gaurav Nigam, David A. Clifton
First submitted to arxiv on: 13 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 Machine learning algorithms hold promise for personalized medicine, but their clinical adoption remains limited due to biases that can compromise the reliability of predictions. Our paper focuses on sample selection bias (SSB), a type of bias where the study population is less representative of the target population, leading to biased and potentially harmful decisions. We examine SSB’s impact on machine learning algorithm performance and propose two independent networks (T-Net) and a multitasking network (MT-Net) to address SSB by identifying and making predictions for the target subpopulation. Our empirical results show that SSB can lead to a large drop in algorithm performance for the target population, as well as substantial differences in performance for representative subpopulations. We also demonstrate robustness of our proposed techniques across various settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning algorithms are used to help with personalized medicine, but they’re not used as much as they could be because of biases that can make predictions less reliable. In this paper, we look at a specific type of bias called sample selection bias (SSB). SSB happens when the people being studied aren’t representative of the group the algorithm is trying to predict for. We show how SSB can make algorithms perform worse and propose new ways to correct it by identifying and working with the right groups. |
Keywords
» Artificial intelligence » Machine learning
