On the public citation graph
Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study
0 signed · 123 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-11257-2_15openalex-snapshot
Disclosure Risk Evaluation for Fully Synthetic Categorical Data
inferredcites →https://doi.org/10.1007/978-3-319-99771-1_5openalex-snapshot
On the Privacy Guarantees of Synthetic Data: A Reassessment from the Maximum-Knowledge Attacker Perspective
inferredcites →https://doi.org/10.1007/978-3-319-99771-1_9openalex-snapshot
Differential Correct Attribution Probability for Synthetic Data: An Exploration
inferredcites →https://doi.org/10.1016/j.csda.2011.06.006openalex-snapshot
An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets
inferredcites →https://doi.org/10.1016/j.cviu.2018.10.009openalex-snapshot
Pros and cons of GAN evaluation measures
inferredcites →https://doi.org/10.1016/j.ins.2021.12.018openalex-snapshot
Differentially private synthetic medical data generation using convolutional GANs
inferredcites →https://doi.org/10.1016/j.jclinepi.2019.02.004openalex-snapshot
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
inferredcites →https://doi.org/10.1023/a:1010920819831openalex-snapshot
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
inferredcites →https://doi.org/10.1037/pspp0000208openalex-snapshot
Using 26,000 diary entries to show ovulatory changes in sexual desire and behavior.
inferredcites →https://doi.org/10.1038/s41746-020-00353-9openalex-snapshot
Generating high-fidelity synthetic patient data for assessing machine learning healthcare software
inferredcites →https://doi.org/10.1080/09332480.2004.10554907openalex-snapshot
New Approaches to Data Dissemination: A Glimpse into the Future (?)
inferredcites →https://doi.org/10.1080/19345747.2019.1631421openalex-snapshot
The Promise and Limitations of Synthetic Data as a Strategy to Expand Access to State-Level Multi-Agency Longitudinal Data
inferredcites →https://doi.org/10.1093/bioinformatics/btm158openalex-snapshot
Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach
inferredcites →https://doi.org/10.1093/biomet/70.1.41openalex-snapshot
The central role of the propensity score in observational studies for causal effects
inferredcites →https://doi.org/10.1093/jamia/ocaa249openalex-snapshot
Optimizing the synthesis of clinical trial data using sequential trees
inferredcites →https://doi.org/10.1093/jamia/ocz161openalex-snapshot
Ensuring electronic medical record simulation through better training, modeling, and evaluation
inferredcites →https://doi.org/10.1093/jamiaopen/ooaa060openalex-snapshot
Spot the difference: comparing results of analyses from real patient data and synthetic derivatives
inferredcites →https://doi.org/10.1093/jamiaopen/ooab012openalex-snapshot
Evaluating the utility of synthetic COVID-19 case data
inferredcites →https://doi.org/10.1093/oso/9780198509844.001.0001openalex-snapshot
The Statistical Evaluation of Medical Tests for Classification and Prediction
inferredcites →https://doi.org/10.1109/cbms.2019.00036openalex-snapshot
Generating and Evaluating Synthetic UK Primary Care Data: Preserving Data Utility & Patient Privacy
inferredcites →https://doi.org/10.1109/smartgridcomm.2018.8587464openalex-snapshot
Generative Adversarial Network for Synthetic Time Series Data Generation in Smart Grids
inferredcites →https://doi.org/10.1111/j.1467-985x.2004.00343.xopenalex-snapshot
Releasing Multiply Imputed, Synthetic Public use Microdata: An Illustration and Empirical Study
inferredcites →https://doi.org/10.1136/bmjopen-2020-043497openalex-snapshot
Can synthetic data be a proxy for real clinical trial data? A validation study
inferredcites →https://doi.org/10.1145/1143844.1143874openalex-snapshot
The relationship between Precision-Recall and ROC curves
inferredcites →https://doi.org/10.1145/3372297.3417238openalex-snapshot
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
inferredcites →https://doi.org/10.1161/circoutcomes.118.005122openalex-snapshot
Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing
inferredcites →https://doi.org/10.1186/s12874-020-00977-1openalex-snapshot
Generation and evaluation of synthetic patient data
inferredcites →https://doi.org/10.1198/000313006x124640openalex-snapshot
A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality
inferredcites →https://doi.org/10.1287/mnsc.6.4.366openalex-snapshot
Mathematical Methods of Organizing and Planning Production
inferredcites →https://doi.org/10.14778/3231751.3231757openalex-snapshot
Data synthesis based on generative adversarial networks
inferredcites →https://doi.org/10.2139/ssrn.3623086openalex-snapshot
Conditional Sig-Wasserstein GANs for Time Series Generation
inferredcites →https://doi.org/10.2196/16492openalex-snapshot
Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison From Five Observational Studies
inferredcites →https://doi.org/10.2196/18910openalex-snapshot
Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing
inferredcites →https://doi.org/10.2196/23139openalex-snapshot
Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation
inferredcites →https://doi.org/10.2478/popets-2019-0067openalex-snapshot
Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models
inferredcites →https://doi.org/10.29012/jpc.v1i1.568openalex-snapshot
Global Measures of Data Utility for Microdata Masked for Disclosure Limitation
inferredcites →https://doi.org/10.29012/jpc.v7i3.407openalex-snapshot
Practical Data Synthesis for Large Samples
inferredcites →https://doi.org/10.3233/sji-150153openalex-snapshot
Providing bespoke synthetic data for the UK Longitudinal Studies and other sensitive data with the synthpop package for R1
inferredcites →https://doi.org/10.3233/sji-160959openalex-snapshot
Releasing synthetic magnitude microdata constrained to fixed marginal totals
inferredcites →https://doi.org/10.3389/fdata.2021.679939openalex-snapshot
Holdout-Based Empirical Assessment of Mixed-Type Synthetic Data
inferredcites →https://doi.org/10.3390/app11052158openalex-snapshot
Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation
inferredcites →https://doi.org/10.3929/ethz-b-000392473openalex-snapshot
Generation of Heterogeneous Synthetic Electronic Health Records using GANs
inferredcites →https://doi.org/10.46300/9101openalex-snapshot
International Journal of Mathematical Models and Methods in Applied Sciences
inferredcites →https://doi.org/10.7554/elife.53275openalex-snapshot
A synthetic dataset primer for the biobehavioural sciences to promote reproducibility and hypothesis generation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpt.3001openalex-snapshot
A Case for Synthetic Data in Regulatory Decision‐Making in Europe
inferred← citeshttps://doi.org/10.1002/pds.70019openalex-snapshot
Validation Assessment of Privacy‐Preserving Synthetic Electronic Health Record Data: Comparison of Original Versus Synthetic Data on Real‐World COVID ‐19 Vaccine Effectiveness
inferred← citeshttps://doi.org/10.1007/978-3-031-27815-0_25openalex-snapshot
Research Paper: Process Mining and Synthetic Health Data: Reflections and Lessons Learnt
inferred← citeshttps://doi.org/10.1007/978-981-16-6775-6_26openalex-snapshot
Synthetic Data as a Tool to Combat Racial Bias in Medical AI: Utilizing Generative Models for Optimizing Early Detection of Melanoma in Fitzpatrick Skin Types IV–VI
inferred← citeshttps://doi.org/10.1007/978-981-96-3041-7_30openalex-snapshot
Utilization of AI in Designing RNA Therapeutics
inferred← citeshttps://doi.org/10.1007/978-981-96-4883-2_13openalex-snapshot
LODHS: Linked Open Data Generation Using Human-in-the-Middle
inferred← citeshttps://doi.org/10.1007/s10618-024-01081-4openalex-snapshot
Syntheval: a framework for detailed utility and privacy evaluation of tabular synthetic data
inferred← citeshttps://doi.org/10.1007/s41894-025-00165-8openalex-snapshot
Synthetic data as a tool for prototyping early-stage periodontitis detection models
inferred← citeshttps://doi.org/10.1007/s43681-025-00819-0openalex-snapshot
An assessment of synthetic data generation, use and disclosure under Canadian privacy regulations
inferred← citeshttps://doi.org/10.1016/j.array.2026.100684openalex-snapshot
Model-based evaluation of synthetic financial time series data: A comparative study with multi-metric validation
inferred← citeshttps://doi.org/10.1016/j.asoc.2026.116553openalex-snapshot
TabEva: Unified tabular data synthesis evaluation framework
inferred← citeshttps://doi.org/10.1016/j.atech.2023.100361openalex-snapshot
A trade-off between farm production and flood alleviation using land use tillage preferences as a natural flood management (NFM) strategy
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108734openalex-snapshot
Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review
inferred← citeshttps://doi.org/10.1016/j.compenvurbsys.2024.102242openalex-snapshot
Activity-based simulations for neighbourhood planning towards social-spatial equity
inferred← citeshttps://doi.org/10.1016/j.cosrev.2023.100546openalex-snapshot
Synthetic data generation: State of the art in health care domain
inferred← citeshttps://doi.org/10.1016/j.egyr.2025.05.062openalex-snapshot
Generative AI applied for synthetic data in PMU
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126950openalex-snapshot
Artificial generation of survey data on the expected bitterness of beer
inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2024.105413openalex-snapshot
Can I trust my fake data – A comprehensive quality assessment framework for synthetic tabular data in healthcare
inferred← citeshttps://doi.org/10.1016/j.isci.2022.105331openalex-snapshot
Synthetic data as an enabler for machine learning applications in medicine
inferred← citeshttps://doi.org/10.1016/j.jdent.2025.106274openalex-snapshot
Synthetic imaging in dentistry: A narrative review of deep learning techniques and applications
inferred← citeshttps://doi.org/10.1016/j.jval.2025.04.2157openalex-snapshot
Roles of Artificial Intelligence--Based Synthetic Data in Health Economics and Outcomes Research
inferred← citeshttps://doi.org/10.1016/j.mlwa.2024.100608openalex-snapshot
Sharing is CAIRing: Characterizing principles and assessing properties of universal privacy evaluation for synthetic tabular data
inferred← citeshttps://doi.org/10.1016/j.tips.2023.06.010openalex-snapshot
AI-powered therapeutic target discovery
inferred← citeshttps://doi.org/10.1038/s41597-026-07910-zopenalex-snapshot
Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment
inferred← citeshttps://doi.org/10.1038/s41598-024-51268-4openalex-snapshot
Entropy removal of medical diagnostics
inferred← citeshttps://doi.org/10.1038/s41598-024-57207-7openalex-snapshot
An evaluation of the replicability of analyses using synthetic health data
inferred← citeshttps://doi.org/10.1038/s41598-024-69812-7openalex-snapshot
Synthesis and quality assessment of combined time-series and static medical data using a real-world time-series generative adversarial network
inferred← citeshttps://doi.org/10.1038/s41746-024-01409-wopenalex-snapshot
A review on generative AI models for synthetic medical text, time series, and longitudinal data
inferred← citeshttps://doi.org/10.1051/medsci/2024091openalex-snapshot
Données synthétiques en médecine : génération, évaluation et limites
inferred← citeshttps://doi.org/10.1093/jamia/ocac131openalex-snapshot
Keeping synthetic patients on track: feedback mechanisms to mitigate performance drift in longitudinal health data simulation
inferred← citeshttps://doi.org/10.1093/jamia/ocaf169openalex-snapshot
Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data
inferred← citeshttps://doi.org/10.1093/jamiaopen/ooac083openalex-snapshot
Validating a membership disclosure metric for synthetic health data
inferred← citeshttps://doi.org/10.1093/jamiaopen/ooae114openalex-snapshot
Large language models and synthetic health data: progress and prospects
inferred← citeshttps://doi.org/10.1109/access.2024.3366556openalex-snapshot
Trading Off Scalability, Privacy, and Performance in Data Synthesis
inferred← citeshttps://doi.org/10.1109/access.2025.3532128openalex-snapshot
Comprehensive Review of Privacy, Utility, and Fairness Offered by Synthetic Data
inferred← citeshttps://doi.org/10.1109/eurospw67616.2025.00034openalex-snapshot
Explaining and Visualizing Synthetic Data Quality Using Statistical Distances
inferred← citeshttps://doi.org/10.1109/ictai66417.2025.00167openalex-snapshot
A Robust Metric for Evaluating the Quality of Synthetic Tabular Data
inferred← citeshttps://doi.org/10.1109/ismsit67332.2025.11268001openalex-snapshot
Generative Adversarial Networks-Driven Synthetic Patient Data Creation for Risk Identification in Childhood Acute Lymphoblastic Leukemia
inferred← citeshttps://doi.org/10.1109/jbhi.2023.3236722openalex-snapshot
Characterization of Synthetic Health Data Using Rule-Based Artificial Intelligence Models
inferred← citeshttps://doi.org/10.1109/ojemb.2024.3426910openalex-snapshot
Introducing SPINE: A Holistic Approach to Synthetic Pulmonary Imaging Evaluation Through End-to-End Data and Model Management
inferred← citeshttps://doi.org/10.1145/3636424openalex-snapshot
A Survey of Generative Adversarial Networks for Synthesizing Structured Electronic Health Records
inferred← citeshttps://doi.org/10.1145/3704437openalex-snapshot
Systematic Review of Generative Modelling Tools and Utility Metrics for Fully Synthetic Tabular Data
inferred← citeshttps://doi.org/10.1186/s12874-023-01869-wopenalex-snapshot
A method for generating synthetic longitudinal health data
inferred← citeshttps://doi.org/10.1186/s12874-026-02909-xopenalex-snapshot
Real-time prediction of trauma-induced coagulopathy using an inverted transformer (trauma-former): a methodological feasibility and simulation study based on the ADEMP framework
inferred← citeshttps://doi.org/10.1186/s12911-024-02731-9openalex-snapshot
How good is your synthetic data? SynthRO, a dashboard to evaluate and benchmark synthetic tabular data
inferred← citeshttps://doi.org/10.1200/cci-26-00014openalex-snapshot
Enhancing Outcome Measurement in Oncology Clinical Trials Through Artificial Intelligence: A Scoping Review
inferred← citeshttps://doi.org/10.1200/cci.23.00021openalex-snapshot
Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology
inferred← citeshttps://doi.org/10.1200/cci.23.00071openalex-snapshot
Status of Synthetic Data Generation for Structured Health Data
inferred← citeshttps://doi.org/10.1371/journal.pdig.0001522openalex-snapshot
Evaluating the quality of tabular synthetic data in health care
inferred← citeshttps://doi.org/10.20948/prepr-2024-53openalex-snapshot
On the estimation of integral risk of predictor Lipschitz functions in machine learning models
inferred← citeshttps://doi.org/10.2196/66821openalex-snapshot
Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study
inferred← citeshttps://doi.org/10.2196/77893openalex-snapshot
Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study
inferred← citeshttps://doi.org/10.2196/preprints.55118openalex-snapshot
A Comparison of Synthetic Data Generation Techniques for Control Group Survival Data in Oncology Clinical Trials: Simulation Study (Preprint)
inferred← citeshttps://doi.org/10.2196/preprints.56021openalex-snapshot
Synthesis and Quality Assessment of Combined Time-Series and Static Medical Data Using a Real-world Time-Series Generative Adversarial Network (Preprint)
inferred← citeshttps://doi.org/10.26686/wgtn.27014419openalex-snapshot
Data-Centric AI: Tabular Data Synthesis with Deep Generative Models
inferred← citeshttps://doi.org/10.3233/shti240511openalex-snapshot
Synthetic Generation of Patient Service Utilization Data: A Scalability Study
inferred← citeshttps://doi.org/10.3389/fdgth.2025.1576290openalex-snapshot
Comprehensive evaluation framework for synthetic tabular data in health: fidelity, utility and privacy analysis of generative models with and without privacy guarantees
inferred← citeshttps://doi.org/10.3389/fdgth.2026.1833779openalex-snapshot
A call for principle-based acceptance of synthetic patients: establishing the AI-driven foundation for regulatory submissions
inferred← citeshttps://doi.org/10.3389/fmed.2026.1947584openalex-snapshot
AI-driven quality enhancement of medical datasets: a closed-loop framework of synthesis, annotation, quality control, and integration
inferred← citeshttps://doi.org/10.3389/frai.2025.1533508openalex-snapshot
Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data
inferred← citeshttps://doi.org/10.3389/fspor.2025.1607600openalex-snapshot
A synthetic data-driven machine learning approach for athlete performance attenuation prediction
inferred← citeshttps://doi.org/10.3389/fspor.2025.1646146openalex-snapshot
Gait stability prediction through synthetic time-series and vision-based data
inferred← citeshttps://doi.org/10.3390/electronics11203277openalex-snapshot
Statistical Validation of Synthetic Data for Lung Cancer Patients Generated by Using Generative Adversarial Networks
inferred← citeshttps://doi.org/10.4018/979-8-2600-2845-2.ch005openalex-snapshot
Topological Auditing of Synthetic Public Data for Trustworthy Governance
inferred← citeshttps://doi.org/10.4018/979-8-3373-2277-3.ch003openalex-snapshot
Digital Synthetic Generation From Learning and BigData
inferred← citeshttps://doi.org/10.4018/979-8-3693-0255-2.ch005openalex-snapshot
Synthetic Data Generation
inferred← citeshttps://doi.org/10.4995/thesis/10251/204747openalex-snapshot
An Architecture for Crowd Density Estimation in Heterogenous Opportunistic Environment
inferred← citeshttps://doi.org/10.5753/errc.2025.17810openalex-snapshot
Reducing Instability in Synthetic Data Evaluation with a Super-Metric in MalDataGen
inferred← citeshttps://doi.org/10.5753/sbcas.2026.21722openalex-snapshot
Redes Bayesianas para Geração de Dados Totalmente Sintéticos para Alta do Recém-Nascido: Uma Proposta Metodológica Orientada a Conhecimento
inferred← citeshttps://doi.org/10.6084/m9.figshare.26577188openalex-snapshot
Additional file 1 of A method for generating synthetic longitudinal health data
inferred← citeshttps://doi.org/10.64898/2026.06.12.26355456openalex-snapshot
Evaluation of AI-Generated Synthetic Data for Clinical Research in Secondary Cardiovascular Prevention among Dyslipidemia Patients
inferred← citeshttps://doi.org/10.64898/2026.08.03.742474openalex-snapshot
Synthetic Longitudinal Tabular Data Generation via Copula
inferred← citeshttps://doi.org/10.64898/2026.08.31.26361878openalex-snapshot
Evaluating synthetic-data fidelity in two-group biomedical studies: a multidimensional validation framework
inferred← citeshttps://doi.org/10.70162/mijarcse/2025/v11/i1/v11i118openalex-snapshot
Synthetic Clinical Cohorts from Generative Models: Utility-Privacy Tradeoffs, Validation Protocols, and Regulatory Compliance
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.