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SPRINT_gan

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Privacy-preserving generative deep neural networks support clinical data sharing

파일 탐색기

최종 버전 다운로드 (.zip)

- __init__.py
- noisy_optimizers.py
- Dockerfile
- .gitkeep
- Analysis.html
- Figure_1D.png
- Figure_1E.png
- Figure_2A.png
- Figure_2B.png
- Figure_2C.png
- Figure_2D.png
- Figure_3A.png
- Figure_3B.png
- Figure_3C.png
- Figure_4A.png
- Figure_4B.png
- Figure_4C.png
- Figure_4D.png
- Figure_4E.png
- Figure_4F.png
- Figure_4G.png
- Figure_5.png
- Supplemental_Figure_2A.png
- Supplemental_Figure_2B.png
- Supplemental_Figure_3A.png
- Supplemental_Figure_3B.png
- - Analysis-checkpoint.ipynb
  - PreprocessMIMIC-checkpoint.ipynb
- .DS_Store
- Analysis.ipynb
- MIMICAnalysis.ipynb
- PreprocessMIMIC.ipynb
- __init__.py
- accountant.py
- gaussian_moment.py
- utils.py
.drone.yml
.DS_Store
.git_ignore
.gitignore
ac_gan.py
Analysis.ipynb
dp_gan.py
LICENSE.md
mimic_ac_gan.py
mimic_dp_gan.py
noise_sweep.sh
PredictGroup.ipynb
Preprocess.ipynb
priv_calc.py
priv_jobs.sh
QualitativeAnalysis.ipynb
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/greenelab/SPRINT_gan

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd SPRINT_gan

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Docker

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Docker Desktop 컨테이너를 빌드하고 실행하려면 필요합니다. 설치 후 실행해서 백그라운드에 켜두세요.

docker build -f docker/Dockerfile -t sprint-gan .

Dockerfile을 기반으로 실행 가능한 이미지를 빌드합니다.

docker run -p 8080:80 sprint-gan

빌드된 이미지를 실제 컨테이너로 실행합니다.

✅ 터미널에 docker compose ps 를 입력해 컨테이너들이 Up 상태인지 확인하세요. README에 포트 번호가 적혀있다면 브라우저에서 http://localhost:포트번호 로 접속해보세요.

// repository documentation

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# Privacy-preserving generative deep neural networks support clinical data sharing

Brett K. Beaulieu-Jones<sup>1</sup>, Zhiwei Steven Wu<sup>2</sup>, Chris Williams<sup>3</sup>, Casey S. Greene<sup>3</sup>*

<sup>1</sup>Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

<sup>2</sup>Computer and Information Science, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

<sup>3</sup>Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

To whom correspondence should be addressed: csgreene (at) upenn.edu

### https://doi.org/10.1101/159756

Introduction
--------
Though it is widely recognized that data sharing enables faster scientific progress, the sensible need to protect participant privacy hampers this practice in medicine. We train deep neural networks that generate synthetic subjects closely resembling study participants. Using the SPRINT trial as an example, we show that machine-learning models built from simulated participants generalize to the original dataset. We incorporate differential privacy, which offers strong guarantees on the likelihood that a subject could be identified as a member of the trial. Investigators who have compiled a dataset can use our method to provide a freely accessible public version that enables other scientists to perform discovery-oriented analyses. Generated data can be released alongside analytical code to enable fully reproducible workflows, even when privacy is a concern. By addressing data sharing challenges, deep neural networks can facilitate the rigorous and reproducible investigation of clinical datasets.

First run Preprocess.ipynb on the SPRINT clinical trial data (https://challenge.nejm.org/pages/home)

The results in the paper use:

`python dp_gan.py --noise 1 --clip_value 0.0001 --epochs 500 --lr 2e-05 --batch_size 1 --prefix 1_0.0001_2e-05_ `

Due to GPU threading, the current lack of an effective way to set a random seed for tensorflow, and the fact that we are optimizing two neural networks (unlikely to find global minima) it may require running multiple times to hit a good local minimum.

<img src="https://github.com/greenelab/SPRINT_gan/raw/master/figures/Figure_2A.png?raw=true" alt="Fig 2A" width="300"/><img src="https://github.com/greenelab/SPRINT_gan/raw/master/figures/Figure_2B.png?raw=true" alt="Fig 2B" width="300"/>
<img src="https://github.com/greenelab/SPRINT_gan/raw/master/figures/Figure_2C.png?raw=true" alt="Fig 2C" width="300"/><img src="https://github.com/greenelab/SPRINT_gan/raw/master/figures/Figure_2D.png?raw=true" alt="Fig 2D" width="300"/>

**Median Systolic Blood Pressure Trajectories from initial visit to 27 months.** **A.)** Simulated samples (private and non-private) generated from the final (500th) epoch of training. **B.)** Simulated samples generated from the epoch with the best performing logistic regression classifier. **C.)** Simulated samples from the epoch with the best performing random forest classifier. **D.)** Simulated samples from the top five random forest classifier epochs and top five logistic regression classifier epochs.

Feedback
--------

Please feel free to email us - (brettbe) at med.upenn.edu with any feedback or raise a github issue with any comments or questions.

Acknowledgements
----------------
Acknowledgments: We thank Jason H. Moore (University of Pennsylvania), Aaron Roth (University of Pennsylvania), Gregory Way (University of Pennsylvania), Yoseph Barash (University of Pennsylvania) and Anupama Jha (University of Pennsylvania) for their helpful discussions. We also thank the participants of the SPRINT trial and the entire SPRINT Research Group for providing the data used in this study. Funding: This work was supported by the Gordon and Betty Moore Foundation under a Data Driven Discovery Investigator Award to C.S.G. (GBMF 4552). B.K.B.-J. Was supported by a Commonwealth Universal Research Enhancement (CURE) Program grant from the Pennsylvania Department of Health and by US National Institutes of Health grants AI116794 and LM010098. Z.S.W is funded in part by a subcontract on the DARPA Brandeis project and a grant from the Sloan Foundation.