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

2019/10/02 by Daniel Severo, Flávio Amaro, Severo, Daniel +5
Computer Science · Medicine · #Autopsy Techniques and Outcomes #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare

paper · pdf · doi:10.48550/arxiv.1910.00752

openalex publication_date 2019/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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

Citations

• Gradient-based learning applied to document recognition
• Divergence measures based on the Shannon entropy
• Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
• Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record
• TorchGAN: A Flexible Framework for GAN Training and Evaluation

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