ArticleJournal of the American Medical Informatics Association : JAMIA2016
VERTIcal Grid lOgistic regression (VERTIGO).
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- VALORIS: One-shot and lossless vertical logistic regression for privacy-protecting multi-site health analytics.Scientific reports · 2026Article
- Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals.Nature communications · 2025Article
- COLLAGENE enables privacy-aware federated and collaborative genomic data analysis.Genome biology · 2023Article
- Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data.Sensors (Basel, Switzerland) · 2022Article
- Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.Methods of information in medicine · 2022Article
- VERTICOX: Vertically Distributed Cox Proportional Hazards Model Using the Alternating Direction Method of Multipliers.IEEE transactions on knowledge and data engineering · 2022Article
- Selecting Privacy-Enhancing Technologies for Managing Health Data Use.Frontiers in public health · 2022Review
- Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study.JMIR medical informatics · 2021Article
- Automatable Distributed Regression Analysis of Vertically Partitioned Data Facilitated by PopMedNet: Feasibility and Enhancement Study.JMIR medical informatics · 2021Article
- Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions.SN computer science · 2021Article
- Privacy-preserving construction of generalized linear mixed model for biomedical computation.Bioinformatics (Oxford, England) · 2020Article
- Distributed Regression Analysis Application in Large Distributed Data Networks: Analysis of Precision and Operational Performance.JMIR medical informatics · 2020Article
- VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Privacy-Preserving Methods for Vertically Partitioned Incomplete Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- "Après Mois, Le Déluge": Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era.Frontiers in oncology · 2019Review
- Privacy Policy and Technology in Biomedical Data Science.Annual review of biomedical data science · 2018Article
- A Query Workflow Design to Perform Automatable Distributed Regression Analysis in Large Distributed Data Networks.EGEMS (Washington, DC) · 2018Article
- Three controversies in health data science.International journal of data science and analytics · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveTo develop an accurate logistic regression (LR) algorithm to support federated data analysis of vertically partitioned distributed data sets. MATERIAL AND
methodsWe propose a novel technique that solves the binary LR problem by dual optimization to obtain a global solution for vertically partitioned data. We evaluated this new method, VERTIcal Grid lOgistic regression (VERTIGO), in artificial and real-world medical classification problems in terms of the area under the receiver operating characteristic curve, calibration, and computational complexity. We assumed that the institutions could "align" patient records (through patient identifiers or hashed "privacy-protecting" identifiers), and also that they both had access to the values for the dependent variable in the LR model (eg, that if the model predicts death, both institutions would have the same information about death).
resultsThe solution derived by VERTIGO has the same estimated parameters as the solution derived by applying classical LR. The same is true for discrimination and calibration over both simulated and real data sets. In addition, the computational cost of VERTIGO is not prohibitive in practice. DISCUSSION: There is a technical challenge in scaling up federated LR for vertically partitioned data. When the number of patients m is large, our algorithm has to invert a large Hessian matrix. This is an expensive operation of time complexity O(m(3)) that may require large amounts of memory for storage and exchange of information. The algorithm may also not work well when the number of observations in each class is highly imbalanced.
conclusionThe proposed VERTIGO algorithm can generate accurate global models to support federated data analysis of vertically partitioned data.
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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.