A synthetic dataset of liver disorder patients.

The data in this article include 10,000 synthetic patients with liver disorders, characterized by 70 different variables, including clinical features, and patient outcomes, such as hospital admission or surgery. Patient data are generated, simulating as close as possible real patient data, using a publicly available Bayesian network describing a casual model for liver disorders. By varying the network parameters, we also generated an additional set of 500 patients with characteristics that deviated from the initial patient population. We provide an overview of the synthetic data generation process and the associated scripts for generating the cohorts. This dataset can be useful for the machine learning models training and validation, especially under the effect of dataset shift between training and testing sets.

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© 2023 The Authors. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: GN is a full employee of enGenome srl.