Generating virtual patient data for in silico clinical trials of medical devices during extracorporeal membrane oxygenation treatment

Abstract

Abstract This study introduces a virtual patient generation model as online tool through the generation of high-quality synthetic data, addressing challenges like privacy cocerns and limited dataset sizes. Using a Conditional Tabular Generative Adversarial Network (CTGAN), we generated synthetic data from the Electronic Health Records (EHR) of 767 veno-venous extracorporeal membrane oxygenation (ECMO) patients, focusing on 55 critical therapy parameters. Rigorous preprocessing, imputation, and model tuning ensured that the synthetic data closely mirrored real patient records, achieving a 86.6% coverage score and minimal deviations in data correlations. The tool is integrated into a web platform - https://cve-sim.de/ecmo-vpg , allowing researchers to generate and visualize virtual patient cohorts, potentially enhancing ECMO research and reducing the need for extensive clinical trials.

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