ArticleThe annals of applied statistics2023
TARGETING UNDERREPRESENTED POPULATIONS IN PRECISION MEDICINE: A FEDERATED TRANSFER LEARNING APPROACH.
Article in The annals of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed.
- Improved risk prediction via cross-domain calibration in a retrospective case-control study.The Canadian journal of statistics = Revue canadienne de statistique · 2026Article
- LEARNER: A Transfer Learning Method for Low-Rank Matrix Estimation.Statistics in medicine · 2026Article
- Bridging Ancestry-Stratified Bias in Pharmacogenomics AI: Toward Metabolomics-Inclusive Multi-Omics Precision Medicine.Journal of personalized medicine · 2026Review
- ReFIT: Federated Transfer Learning for Sequential Prediction and Uncertainty Quantification Using Streaming EHR Data.Statistics in biosciences · 2026Article
- LtransHeteroGGM: local transfer learning for Gaussian graphical model-based heterogeneity analysis.Bioinformatics (Oxford, England) · 2026Article
- Enhancing cause of death prediction: development and validation of machine learning models using multimodal data across multiple health-care sites.JAMIA open · 2026Article
- Multivariate and Online Transfer Learning With Uncertainty Quantification.Statistics in medicine · 2026Article
- Fast and robust invariant generalized linear models.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Robust angle-based transfer learning in high dimensions.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Transdiagnostic Polygenic Risk Models for Psychopathology and Comorbidity: Cross-Ancestry Analysis in themedRxiv : the preprint server for health sciences · 2025Article
- A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.PLoS computational biology · 2025Article
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- Semi-supervised Triply Robust Inductive Transfer Learning.Journal of the American Statistical Association · 2025Article
- Article
- Polygenic prediction for underrepresented populations through transfer learning by utilizing genetic similarity shared with European populations.Briefings in bioinformatics · 2024Article
- Enhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data.Statistics in biosciences · 2024Article
- Multi-Source Conformal Inference Under Distribution Shift.Proceedings of machine learning research · 2024Article
- Article
- Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST).Journal of machine learning research : JMLR · 2024Article
- Trans-Balance: Reducing demographic disparity for prediction models in the presence of class imbalance.Journal of biomedical informatics · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The limited representation of minorities and disadvantaged populations in large-scale clinical and genomics research poses a significant barrier to translating precision medicine research into practice. Prediction models are likely to underperform in underrepresented populations due to heterogeneity across populations, thereby exacerbating known health disparities. To address this issue, we propose FETA, a two-way data integration method that leverages a federated transfer learning approach to integrate heterogeneous data from diverse populations and multiple healthcare institutions, with a focus on a target population of interest having limited sample sizes. We show that FETA achieves performance comparable to the pooled analysis, where individual-level data is shared across institutions, with only a small number of communications across participating sites. Our theoretical analysis and simulation study demonstrate how FETA's estimation accuracy is influenced by communication budgets, privacy restrictions, and heterogeneity across populations. We apply FETA to multisite data from the electronic Medical Records and Genomics (eMERGE) Network to construct genetic risk prediction models for extreme obesity. Compared to models trained using target data only, source data only, and all data without accounting for population-level differences, FETA shows superior predictive performance. FETA has the potential to improve estimation and prediction accuracy in underrepresented populations and reduce the gap in model performance across populations.
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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.