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Controllable Synthetic Clinical Note Generation with Privacy Guarantees

Tal Baumel, Andre Manoel, Daniel Jones, Shize Su, Huseyin Inan, Aaron, Bornstein, Robert Sim

TL;DR

A novel method to cloned datasets containing Personal Health Information (PHI) is introduced, offering a viable solution for the ethical and effective utilization of sensitive medical data in machine learning, facilitating progress in medical research and the development of robust predictive models.

Abstract

In the field of machine learning, domain-specific annotated data is an invaluable resource for training effective models. However, in the medical domain, this data often includes Personal Health Information (PHI), raising significant privacy concerns. The stringent regulations surrounding PHI limit the availability and sharing of medical datasets, which poses a substantial challenge for researchers and practitioners aiming to develop advanced machine learning models. In this paper, we introduce a novel method to "clone" datasets containing PHI. Our approach ensures that the cloned datasets retain the essential characteristics and utility of the original data without compromising patient privacy. By leveraging differential-privacy techniques and a novel fine-tuning task, our method produces datasets that are free from identifiable information while preserving the statistical properties necessary for model training. We conduct utility testing to evaluate the performance of machine learning models trained on the cloned datasets. The results demonstrate that our cloned datasets not only uphold privacy standards but also enhance model performance compared to those trained on traditional anonymized datasets. This work offers a viable solution for the ethical and effective utilization of sensitive medical data in machine learning, facilitating progress in medical research and the development of robust predictive models.

Controllable Synthetic Clinical Note Generation with Privacy Guarantees

TL;DR

A novel method to cloned datasets containing Personal Health Information (PHI) is introduced, offering a viable solution for the ethical and effective utilization of sensitive medical data in machine learning, facilitating progress in medical research and the development of robust predictive models.

Abstract

In the field of machine learning, domain-specific annotated data is an invaluable resource for training effective models. However, in the medical domain, this data often includes Personal Health Information (PHI), raising significant privacy concerns. The stringent regulations surrounding PHI limit the availability and sharing of medical datasets, which poses a substantial challenge for researchers and practitioners aiming to develop advanced machine learning models. In this paper, we introduce a novel method to "clone" datasets containing PHI. Our approach ensures that the cloned datasets retain the essential characteristics and utility of the original data without compromising patient privacy. By leveraging differential-privacy techniques and a novel fine-tuning task, our method produces datasets that are free from identifiable information while preserving the statistical properties necessary for model training. We conduct utility testing to evaluate the performance of machine learning models trained on the cloned datasets. The results demonstrate that our cloned datasets not only uphold privacy standards but also enhance model performance compared to those trained on traditional anonymized datasets. This work offers a viable solution for the ethical and effective utilization of sensitive medical data in machine learning, facilitating progress in medical research and the development of robust predictive models.
Paper Structure (15 sections, 6 figures, 1 table)

This paper contains 15 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Differential Privacy
  4. Differentially Private Stochastic Gradient Descent (DP-SGD)
  5. Training Language Models with Differential Privacy
  6. Clinical Structuring of Unstructured Text
  7. Automatically Generated Instruction Datasets
  8. Method
  9. Results
  10. Privacy-Utility Trade-off in Trained Language Models
  11. Metrics for Privacy Evaluation
  12. Privacy Evaluation
  13. Utility Test
  14. Conclusion
  15. Appendix: Compute Used Overview

Figures (6)

  • Figure 1: Instruction dataset example taken from OpenAI grade-school-math dataset
  • Figure 2: Tuning model over examples from the instruct dataset and various use scenarios
  • Figure 3: Instruct dataset generation
  • Figure 4: Perplexity over time of RoBERTa MLM train on each document set calculate against the original MIMIC texts
  • Figure 5: Train loss over time of RoBERTa MLM train on each document set
  • ...and 1 more figures

Theorems & Definitions (1)

  • Definition 1: Differential Privacy (DP) DworkMNS06DworkKMMN06