A Case for Synthetic Data in Regulatory Decision‐Making in Europe

Alloza, Clara; Knox, Bethany; Raad, Hanaya; Aguilà, Mireia; Coakley, Ciara; Mohrova, Zuzana; Boin, Élodie; Bénard, Marc; Davies, Jessica; Jacquot, Emmanuelle; Lecomte, Coralie; Fabre, Alban; Batech, Michael

doi:10.1002/cpt.3001

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…The adoption of synthetic data is becoming a feasible solution to this problem (Alloza et al, 2023;El Emam et al, 2020;Ladeira Marques et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Split Cross-Strategy for Enhancing Machine Learning Algorithms Prediction Results with Data Generated by Conditional Generative Adversarial Network

Abassi,

Bakkas,

Bakkas

et al. 2024

Journal of Computer Science

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In this study, we present a Multi-Split Cross-Strategy (MSC-Strategy) designed to leverage synthetic tabular data generated by a Conditional Generative Adversarial Network (CGAN). Our study aims to investigate the potential of synthetic data in comparison to real-world data for improving machine learning predictive results. Firstly, we develop a CGAN architecture tailored to generate synthetic tabular data, trained on a comprehensive real-world dataset. Secondly, we validate the synthetic data generated by the CGAN to ensure its statistical fidelity and resemblance to the distribution of real data. Finally, we selectively leverage a subset of the generated data and apply our strategy to create a new combined training set comprising the training set of real data and the chosen subset of generated data. To validate our approach, we employ six diverse regression models: Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), XGB Regressor (XGB), and Support Vector Regressor (SVR). Each model is trained and tested using a training set of real data, generated data, combined data (training set of real data and generated data), and data formed by our MSC strategy. Our findings indicate that the training set formed by our MSC strategy demonstrates remarkable predictive performance compared to real-world data and generated data, highlighting its ability to enhance the prediction of machine learning models using only a subset of generated data.

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“…The adoption of synthetic data is becoming a feasible solution to this problem (Alloza et al, 2023;El Emam et al, 2020;Ladeira Marques et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Split Cross-Strategy for Enhancing Machine Learning Algorithms Prediction Results with Data Generated by Conditional Generative Adversarial Network

Abassi,

Bakkas,

Bakkas

et al. 2024

Journal of Computer Science

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Decades in the making: A wet lab for digital health and the evolution of research data infrastructure (Preprint)

Austin,

Lobo,

Samadbeik

et al. 2024

Preprint

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UNSTRUCTURED Traditionally, medical research is based on randomised controlled trials (RCTs) for interventions such as drugs and operative procedures. However, increasingly there is a need for health research to evolve. RCTs are expensive to run, are generally formulated with a single research question in mind and analyse a limited data set for a restricted time period. Progressively, health decision makers are focussing on the real-world data (RWD) to deliver large scale longitudinal insights that are actionable. RWD is collected as part of routine care in real-time using digital health infrastructure. For example, understanding the effectiveness of an intervention can be enhanced by combining evidence from traditional RCTs with RWD, providing insights into long-term outcomes in real-life situations. Clinicians and researchers struggle in the digital era to harness RWD for digital health research in an efficient, ethically, and morally appropriate manner. This struggle encompasses challenges such as ensuring data quality, integrating diverse sources, establishing governance policies, ensuring regulatory compliance, developing analytical capabilities, and translating insights into actionable strategies. The same way that drug trials require infrastructure to support their conduct, digital health also necessitates new and disruptive research data infrastructure. Novel methods, such as common data models, federated learning and synthetic data generation are emerging to enhance the utility of research using RWD often siloed across health systems. A continued focus on data privacy and ethical compliance remains. Establishing strong cross-sector collaborations among healthcare and academic institutes will further promote a learning health system. The past 25 years have seen a notable shift from sole emphasis on traditional medical research to the inclusion of modern-day methods, harnessing RWD. This paper describes the evolution of research infrastructure developed to support digital health research and shares the lessons learned as a model for other jurisdictions with similar RWD infrastructure requirements.

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A Case for Synthetic Data in Regulatory Decision‐Making in Europe

Cited by 2 publications

References 45 publications

A Multi-Split Cross-Strategy for Enhancing Machine Learning Algorithms Prediction Results with Data Generated by Conditional Generative Adversarial Network

A Multi-Split Cross-Strategy for Enhancing Machine Learning Algorithms Prediction Results with Data Generated by Conditional Generative Adversarial Network

Decades in the making: A wet lab for digital health and the evolution of research data infrastructure (Preprint)

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