Can large language models be privacy preserving and fair medical coders?
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916512817741824 |
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| author | Dadsetan, Ali Soleymani, Dorsa Zeng, Xijie Rudzicz, Frank |
| author_facet | Dadsetan, Ali Soleymani, Dorsa Zeng, Xijie Rudzicz, Frank |
| contents | Protecting patient data privacy is a critical concern when deploying machine learning algorithms in healthcare. Differential privacy (DP) is a common method for preserving privacy in such settings and, in this work, we examine two key trade-offs in applying DP to the NLP task of medical coding (ICD classification). Regarding the privacy-utility trade-off, we observe a significant performance drop in the privacy preserving models, with more than a 40% reduction in micro F1 scores on the top 50 labels in the MIMIC-III dataset. From the perspective of the privacy-fairness trade-off, we also observe an increase of over 3% in the recall gap between male and female patients in the DP models. Further understanding these trade-offs will help towards the challenges of real-world deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05533 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can large language models be privacy preserving and fair medical coders? Dadsetan, Ali Soleymani, Dorsa Zeng, Xijie Rudzicz, Frank Machine Learning Cryptography and Security Protecting patient data privacy is a critical concern when deploying machine learning algorithms in healthcare. Differential privacy (DP) is a common method for preserving privacy in such settings and, in this work, we examine two key trade-offs in applying DP to the NLP task of medical coding (ICD classification). Regarding the privacy-utility trade-off, we observe a significant performance drop in the privacy preserving models, with more than a 40% reduction in micro F1 scores on the top 50 labels in the MIMIC-III dataset. From the perspective of the privacy-fairness trade-off, we also observe an increase of over 3% in the recall gap between male and female patients in the DP models. Further understanding these trade-offs will help towards the challenges of real-world deployment. |
| title | Can large language models be privacy preserving and fair medical coders? |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2412.05533 |