Article

A Method for Machine Learning Generation of Realistic Synthetic Datasets for Validating Healthcare Applications

2021-02-12

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Background</h4> 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...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: bcb04002-8a05-5bac-b459-d5b990db2d98
DOI: 10.1101/2021.02.11.21250741

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A Method for Machine Learning Generation of Realistic Synthetic Datasets for Validating Healthcare ApplicationsDOI 10.1101/2021.02.11.21250741Read

Generating synthetic electronic health record data: a methodological scoping review with benchmarking on phenotype data and open-source software.Semantic proximity Read

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

Generating Patient’s Electronic Health Records with Unseen Diseases Using Ontology-enhanced Generative Adversarial NetworksSemantic proximity Read

Exploring the Role of Synthetic Data in the Future of AI in Healthcare: A Scoping Review of Frameworks, Challenges, and ImplicationsSemantic proximity Read

Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-hoc Interpretability of the Risk FactorsSemantic proximity Read

Synthesising artificial patient-level data for Open Science - an evaluation of five methodsSemantic proximity Read

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?Semantic proximity Read

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?Semantic proximity Read

Data Quality Verification Metrics in Medicine: Experiments and Evaluations from the Perspectives of Safety and UtilitySemantic proximity Read

The Synthetic Fidelity–Stability Framework (SFSF): A Systematic Multi-Dimensional Benchmark of Synthetic Clinical Laboratory Data GeneratorsSemantic proximity Read

Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial IntelligenceSemantic proximity Read

Generative AI Mitigates Representation Bias and Improves Model Fairness Through Synthetic Health DataSemantic proximity Read