Article

Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence

2023-11-08

Abstract excerpt

Clinical research relies on high-quality patient data, however, obtaining big data sets is costly and access to existing data is often hindered by privacy and regulatory concerns. Synthetic data generation holds the promise of effectively bypassing these boundaries allowing for simplified data accessibility and the prospect of synthetic control cohorts. We employed two different methodologies of generative artific...

Topics

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

Identifiers and source

Literature Corpus work: 8fd2fb1e-ff35-525e-8268-94ee5bb81329
DOI: 10.1101/2023.11.08.23298247

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial IntelligenceDOI 10.1101/2023.11.08.23298247Read

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

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

In Silico Trial Simulation with Artificial Intelligence-Generated Synthetic Control Cohorts Reproduces Results of a Randomized Controlled Trial in Acute Myeloid LeukemiaSemantic proximity Read

Revolutionizing Personalized Medicine with Generative AI: A Systematic ReviewSemantic proximity Read

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

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

TabGraphSyn: Graph-Guided Latent Diffusion for High-Fidelity and Privacy-Conscious Clinical Data GenerationSemantic proximity Read

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

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

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

Predictive Modeling of Clinical Trial Outcomes for Novel Drugs using Digital Twin Patient Cohorts and GenerativeAISemantic proximity Read

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