Summary of Examining Imbalance Effects on Performance and Demographic Fairness Of Clinical Language Models, by Precious Jones et al.
Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Models
by Precious Jones, Weisi Liu, I-Chan Huang, Xiaolei Huang
First submitted to arxiv on: 23 Dec 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 The paper investigates how data imbalance affects the performance of language models in biomedical applications, particularly in ICD code prediction tasks where demographic distributions are uneven. State-of-the-art language models have been adopted in biomedical tasks, but few studies have examined the relationship between data imbalance and model performance across demographic groups. This study fills the gap by analyzing imbalances in a standard benchmark dataset using diverse performance metrics and statistical analyses. The results show that data imbalance significantly impacts model performance and fairness, highlighting the importance of developing more equitable and robust language models in healthcare applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how biased data affects the performance of computer programs that help doctors diagnose patients. Doctors often need to guess what’s wrong with a patient based on limited information, which can be tricky. The researchers found that when the data is unbalanced (e.g., more men than women are represented), it affects how well the program works and who it favors. They hope their findings will help create better programs that are fairer and more accurate. |
