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We present a proxy dataset of vital signs with class labels indicatingpatient transitions from the ward to intensive care units called Ward2ICU.Patient privacy is protected using a Wasserstein Generative Adversarial Networkto implicitly learn an approximation of the data distribution, allowing us tosample synthetic data. The quality of data generation is assessed directly onthe binary classification task by comparing specificity and sensitivity of anLSTM classifier on proxy and original datasets. We initialize a discussion ofunintentionally disclosing commercial sensitive information and propose asolution for a special case through class label balancing

Ward2ICU: A Vital Signs Dataset of Inpatients from the General Ward