ArticleFrontiers in big data2020
Variational Autoencoder Modular Bayesian Networks for Simulation of Heterogeneous Clinical Study Data.
Article in Frontiers in big data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Synthetic data generation methods for longitudinal and time series health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Methods for Generating and Evaluating Synthetic Longitudinal Patient Data: A Systematic Review.Journal of healthcare informatics research · 2026Review
- Variational Deep Alliance: A Generative Auto-Encoding Approach to Longitudinal Data Analysis.Entropy (Basel, Switzerland) · 2026Article
- Identification of Post-Ictal Generalised EEG Suppression with Two-Channel EEG.Sensors (Basel, Switzerland) · 2025Article
- A stratified treatment algorithm in psychiatry: a program on stratified pharmacogenomics in severe mental illness (Psych-STRATA): concept, objectives and methodologies of a multidisciplinary project funded by Horizon Europe.European archives of psychiatry and clinical neuroscience · 2025Article
- Article
- Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study.Journal of medical Internet research · 2025Article
- A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases.Biomolecules · 2024Review
- Synthetic data generation for a longitudinal cohort study - evaluation, method extension and reproduction of published data analysis results.Scientific reports · 2024Article
- [Artificial intelligence and secure use of health data in the KI-FDZ project: anonymization, synthetization, and secure processing of real-world data].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024Review
- Towards new frontiers of healthcare systems research using artificial intelligence and generative AI.Health systems (Basingstoke, England) · 2024Article
- A method for generating synthetic longitudinal health data.BMC medical research methodology · 2023Article
- AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease.PLoS computational biology · 2023Article
- Article
- Generation of realistic synthetic data using Multimodal Neural Ordinary Differential Equations.NPJ digital medicine · 2022Article
- SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data.Patterns (New York, N.Y.) · 2022Article
- Federated horizontally partitioned principal component analysis for biomedical applications.Bioinformatics advances · 2022Article
- Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation.Journal of medical Internet research · 2020Article
- Data science in neurodegenerative disease: its capabilities, limitations, and perspectives.Current opinion in neurology · 2020Review
- Challenges of Integrative Disease Modeling in Alzheimer's Disease.Frontiers in molecular biosciences · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the area of Big Data, one of the major obstacles for the progress of biomedical research is the existence of data "silos" because legal and ethical constraints often do not allow for sharing sensitive patient data from clinical studies across institutions. While federated machine learning now allows for building models from scattered data of the same format, there is still the need to investigate, mine, and understand data of separate and very differently designed clinical studies that can only be accessed within each of the data-hosting organizations. Simulation of sufficiently realistic virtual patients based on the data within each individual organization could be a way to fill this gap. In this work, we propose a new machine learning approach [Variational Autoencoder Modular Bayesian Network (VAMBN)] to learn a generative model of longitudinal clinical study data. VAMBN considers typical key aspects of such data, namely limited sample size coupled with comparable many variables of different numerical scales and statistical properties, and many missing values. We show that with VAMBN, we can simulate virtual patients in a sufficiently realistic manner while making theoretical guarantees on data privacy. In addition, VAMBN allows for simulating counterfactual scenarios. Hence, VAMBN could facilitate data sharing as well as design of clinical trials.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.