Author:
Landoll Micha,Huang Yifei,Follegot Filippo,Strassmann Stephan,Steinseifer Ulrich,Karagiannidis Christian,Neidlin Michael
Abstract
AbstractThis 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.
Publisher
Cold Spring Harbor Laboratory
Reference25 articles.
1. Synthetic biomedical data generation in support of In Silico Clinical Trials
2. National Institute on Aging. What Are Clinical Trials and Studies. 2023. url: https://www.nia.nih.gov/health/clinicaltrials-and-studies/what-are-clinical-trials-and-studies.
3. Institute of Medicine (US) Forum on Drug Discovery, Development, and Translation. Challenges in Clinical Research. Available from: https://www.ncbi.nlm.nih.gov/books/NBK50888/. Washington, DC: National Academies Press (US), 2010. xChap. 3.
4. SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data
5. Leveraging electronic health records for data science: common pitfalls and how to avoid them