Ward2ICU: A Vital Signs Dataset of Inpatients from the General Ward
This addresses privacy concerns for healthcare researchers and institutions, but it is incremental as it builds on existing GAN methods for data synthesis.
The authors tackled the problem of patient privacy in medical datasets by creating a synthetic vital signs dataset, Ward2ICU, using a Wasserstein GAN to protect privacy while maintaining data utility, as shown by comparable specificity and sensitivity in an LSTM classifier on proxy vs. original data.
We present a proxy dataset of vital signs with class labels indicating patient transitions from the ward to intensive care units called Ward2ICU. Patient privacy is protected using a Wasserstein Generative Adversarial Network to implicitly learn an approximation of the data distribution, allowing us to sample synthetic data. The quality of data generation is assessed directly on the binary classification task by comparing specificity and sensitivity of an LSTM classifier on proxy and original datasets. We initialize a discussion of unintentionally disclosing commercial sensitive information and propose a solution for a special case through class label balancing