2026-10-14T09:46:53Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33837940 2026-09-16T09:46:07Z portal_1197 item_type_3 month_year_09_2026

Data for A method for machine learning generation of realistic synthetic datasets for validating healthcare applications Theodoros N. Arvanitis (2178638) Sean White (11881602) Stuart Harrison (19972413) Rupert Chaplin (24971323) George Despotou (24862159) Neural networks (Computer science) Machine learning Medical Informatics Medicine -- Data processing Migrated from ePrints Background Digital health applications can improve quality and effectiveness of healthcare, by offering a number of tools to patients, professionals, and the healthcare system. Introduction of new technologies is not without risk, and digital health applications are often considered a medical device. Assuring their safe operation requires, amongst others, clinical validation, which needs large datasets to test their application in realistic clinical scenarios. Access to such datasets is challenging, due to concerns about patient privacy. Development of synthetic datasets, which will be sufficiently realistic to test digital applications, is seen as a potential alternative, enabling their deployment. Objective The aim of work was to develop a method for the generation of realistic synthetic datasets, statistically equivalent to real clinical datasets, and demonstrate that Generative Adversarial Network based approach is fit for purpose. Method A generative adversarial network was implemented and trained, in a series of six experiments, using numerical and categorical variables from three clinically relevant datasets, including ICD-9 and laboratory codes from the MIMIC III dataset. A number of contextual steps provided the success criteria for the synthetic dataset. Results The approach created a synthetic dataset that exhibits very similar statistical characteristics with the real dataset. Pairwise association of variables is very similar. A high degree of Jaccard similarity and a successful K-S test further support this. Conclusions The proof of concept of generating realistic synthetic datasets was successful, with the approach showing promise for further work.<br><br>The dataset contains the synthetic data produced in experiments 3-6, as described in: A Method for Machine Learning Generation of Realistic Synthetic Datasets for Validating Healthcare Applications (DOI: 10.1177/14604582221077000/) in Health Informatics Journal, SAGE. Please contact the 'contacting author' for access. Access after approval of all authors. 2022-02-25T00:00:00Z Dataset Dataset 10.82444/warw.33837940.v1 https://figshare.com/articles/dataset/Data_for_A_method_for_machine_learning_generation_of_realistic_synthetic_datasets_for_validating_healthcare_applications/33837940 CC BY 4.0 Restricted Access