Synthetic Clinical Data Generation

Latest papers

22 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Censored Flow Matching for Synthetic Time-to-Event Generation
Oct 16, 2026Jianbin Tan, Xingran Chen, Kevin He +3Synthetic Data Generation Survival Analysis
Department of Biostatistics & Bioinformatics, Duke University · Department of Biostatistics, University of Michigan · Department of Biostatistics, Yale University +1
DrugTargetWorld: A Synthetic Biobank for Training and Benchmarking AI Scientists
Oct 7, 2026Samuel Margolis, Paul Schmiedmayer, Alan Huang +12Drug Discovery AI Agent Benchmarks
Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA · Department of Medicine, Stanford University, Stanford, CA 94305, USA · Division of Computational Medicine, Department of Medicine, Stanford University, Stanford, CA 94305, USA +1
HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation
Oct 5, 2026Perrine Chassat, Agathe GuillouxStochastic Differential Equations Synthetic Data Generation
Inria, Université Paris Cité, Inserm, HeKA F-75015 Paris, France
Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps
Sep 30, 2026Michael Vasilakakis, Dimitris K. IakovidisSynthetic Data Generation Synthetic Tabular Data Generation
Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
KMGen: A Skill-based Approach for Synthetic Individual Patient Data Generation
Aug 23, 2026Jalen Jiang, Chufan Gao, Ethan Rasmussen +2Synthetic Clinical Data Generation Synthetic Data Generation
Siebel School of Computing and Data Science University of Illinois Urbana-Champaign · Mayo Clinic Alix School of Medicine
CaseWeaver: A Multi-Agent Framework for Multimodal Virtual Clinical Case Generation
Aug 23, 2026Jierui Qu, Jiachuan Peng, Lin Li +2Synthetic Clinical Data Generation Synthetic Data Generation
Biological and Life Sciences Division, MBZUAI, UAE · Department of Engineering Science, University of Oxford, UK · OATML, Department of Computer Science, University of Oxford, UK +1
Dissecting Neuro-Symbolic Quality Assurance for Synthetic Oncology Data Generation
Aug 22, 2026Laxmigayathri Challa, Yuhan Zhou, Ana Cleveland +1Synthetic Clinical Data Generation Synthetic Data Generation
Department of Information Science, University of North Texas, Denton, TX 76203 USA · The Anuradha and Vikas Sinha Department of Data Science, University of North Texas, Denton, TX 76203 USA
CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility
Aug 13, 2026Akanta Das, Farhad Al-Amin Dipto, Mrinmoy Sarkar Anto +3Benchmark Design Synthetic Data Generation
Bangladesh University of Engineering and Technology, Dhaka, Bangladesh · School of Medicine, Stanford University, Stanford, California, United States
SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking
Jul 10, 2026Nicolai Dinh Khang Truong, Richard RöttgerRare Disease Diagnosis Synthetic Data Generation
Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark
A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models
Jun 25, 2026William Poulett, Alice Waterhouse, Ben Wallace +4Synthetic Data Generation Synthetic Clinical Data Generation
NHS England Data Science and Applied AI Team
Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Records
Jun 8, 2026Nicholas I-Hsien Kuo, Blanca Gallego, Louisa JormSynthetic Tabular Data Generation Electronic Health Records
Centre for Big Data Research in Health, the University of New South Wales, Sydney, Australia
Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets
May 18, 2026Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti +8Healthcare Synthetic Tabular Data Generation
University of California, Irvine, CA, USA · PathAI, USA
A Simulated Federated Analysis of MS-Induced Brain Lesions
May 6, 2026Evelyn Trautmann, Joël Federer-Gsponer, Markus C. Elze +1Synthetic Clinical Data Generation Federated Learning
Apheris AI GmbH · F. Hoffmann-La Roche Ltd.
Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation
Apr 29, 2026Guillermo Iglesias, Gema Bello-Orgaz, María Navas-Loro +3Synthetic Data Augmentation Synthetic Data Generation
Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, Spain · Department of Psychiatry, University Hospital Rey Juan Carlos, Mostoles, Spain · Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain +5
Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs
Apr 22, 2026Aishik Mandal, Hiba Arnaout, Clarissa W. Ong +5Mental Health Counseling Mental Health
UKP Lab, Department of Computer Science and Hessian Center for AI (hessian.AI), Technische Universität Darmstadt · Zuse School · National Research Center for Applied Cybersecurity ATHENE +4
Evaluating quality in synthetic data generation for large tabular health datasets
Apr 17, 2026Jean-Baptiste Escudié, Benjamin Barnes, Stefan Meisegeier +3Healthcare Synthetic Tabular Data Generation
Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute · German Centre for Cancer Registry Data, Robert Koch Institute · Medical Informatics, Berlin Institute of Health at Charité - Universitätsmedizin Berlin
Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization
Apr 7, 2026Yanis Labrak, David Grünert, Séverin Baroudi +11Synthetic Data Generation Synthetic Benchmark Generation
Idiap Research Institute, Switzerland · University of Zurich, Switzerland · Université de Toulon, Aix Marseille Univ, LIS, CNRS, France +9
ESL-Bench: An Event-Driven Synthetic Longitudinal Benchmark for Health Agents
Apr 3, 2026Chao Li, Cailiang Liu, Ang Gao +7Synthetic Clinical Data Generation AI Agent Benchmarks
Shanda Group
Knowledge-Guided Retrieval-Augmented Generation for Zero-Shot Psychiatric Data: Privacy Preserving Synthetic Data Generation
Mar 26, 2026Adam Jakobsen, Sushant Gautam, Hugo Lewi Hammer +4Synthetic Clinical Data Generation Synthetic Tabular Data Generation
∗SimulaMet, Norway · †Oslo Metropolitan University, Norway · ‡Uppsala University, Sweden
From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories
Mar 6, 2026Guanglin Zhou, Armin Catic, Motahare Shabestari +4Synthetic Data Generation Synthetic Clinical Data Generation
The University of Queensland, Brisbane, QLD, Australia · The University of New South Wales, Sydney, NSW, Australia · Shahid Sadoughi University of Medical Sciences and Health Services, Yazd, Iran +1
Privacy-Preserving Generation of Clinical Narratives from Medical Terminologies
Sep 13, 2025Yuping Wu, Viktor Schlegel, Warren Del-Pinto +11Medical Report Generation Healthcare
Univeristy of Manchester · Imperial College London, Imperial Global Singapore · Khoo Teck Puat Hospital, Singapore +2
Smooth Flow Matching for Synthesizing Functional Data
Aug 19, 2025Jianbin Tan, Anru R. ZhangFunctional Data Analysis Flow Matching
Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA · Department of Computer Science, Duke University, Durham, NC, USA

Oct 16, 2026J/K move · Enter open · S save

Censored Flow Matching for Synthetic Time-to-Event Generation

Jianbin Tan, Xingran Chen, Kevin He, Zhenke Wu+2

Department of Biostatistics & Bioinformatics, Duke University · Department of Biostatistics, University of Michigan · Department of Biostatistics, Yale University+1

Synthetic Data Generation Survival Analysis Flow-Based Generative Modeling Synthetic Clinical Data Generation

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Generating time-to-event data is increasingly important in survival analysis, but it remains statistically challenging because event times are often censored and thus prevent flexible generative modeling. To fill this gap, we propose a novel method called censored flow matching (CFM), aiming at providing a flexible flow-based generative mechanism for synthesizing time-to-event data as well as learning their corresponding probabilistic densities. Unlike existing methods, CFM does not rely on survival likelihoods to handle censoring, but instead exploits the monotonicity property of differential flows to address censoring via pullback sampling. Under this framework, we implement an alternating procedure between pullback sampling and CFM steps, leading to a flow-based expectation--maximization (EM) algorithm tailored for time-to-event inference. Under mild distributional conditions, we establish the convergence of CFM to the true event-time distribution as the EM iterations proceed. We then apply CFM to generate time-to-event data and estimate their probabilistic densities, where the observed censoring time can be incorporated to provide truncated sampling or truncated density estimation for survival inference. Through extensive simulation studies, we demonstrate the benefits of CFM for complex and large-scale data generation. Finally, we apply CFM to analyze data from the Organ Procurement and Transplantation Network (OPTN) and the Medical Information Mart for Intensive Care IV (MIMIC-IV), showcasing its benefits for synthetic and predictive survival analyses in terms of data fidelity, predictive performance, and empirical privacy characteristics.