Summary of Can Large Language Models Be Privacy Preserving and Fair Medical Coders?, by Ali Dadsetan et al.
Can large language models be privacy preserving and fair medical coders?
by Ali Dadsetan, Dorsa Soleymani, Xijie Zeng, Frank Rudzicz
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 study examines the tension between preserving patient data privacy and maintaining accurate medical coding performance when employing machine learning algorithms in healthcare. The research explores two key trade-offs in applying differential privacy (DP) to the natural language processing task of ICD classification, using the MIMIC-III dataset. The results reveal a significant drop in micro F1 scores for privacy-preserving models, resulting in over 40% reduction on top 50 labels. Furthermore, the study finds an increase in the recall gap between male and female patients in DP models, highlighting the need to balance privacy concerns with fairness considerations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of researchers looked at how machine learning can be used to help doctors classify patient information while keeping that information safe from prying eyes. They tried using a technique called differential privacy (DP) on a specific task called ICD classification. The results showed that DP made the models less good at classifying, losing around 40% of their accuracy. Additionally, they found that the models became more unfair to women than men in terms of patient recall, which is not what you want in healthcare. |
Keywords
» Artificial intelligence » Classification » Machine learning » Natural language processing » Recall
