ArticleNature communications2020
Multiple imputation for analysis of incomplete data in distributed health data networks.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The trial behind it
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
22 citing papers in PubMed, 52 citations in OpenAlex.
- Machine learning for missing data imputation in Alzheimer's research: predicting medial temporal lobe dynamic flexibility.Cognitive neurodynamics · 2026Article
- D3MI: an efficient and powerful federated imputation method for bias reduction in the analysis of distributed incomplete data by accounting for within-site correlation and between-site heterogeneity.BMC medical informatics and decision making · 2026Article
- An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer.Nature communications · 2026Article
- Secure distributed multiple imputation enables missing data inference for private data proprietors.NPJ digital medicine · 2026Article
- Associations of a healthy beverage pattern with all-cause and cause-specific mortality among US adults: a nationwide cohort study.Nutrition journal · 2025Article
- Flexible imputation toolkit for electronic health records.Scientific reports · 2025Article
- Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity.IEEE transactions on knowledge and data engineering · 2025Article
- Prediction model for type 2 diabetes mellitus and its association with mortality using machine learning in three independent cohorts from South Korea, Japan, and the UK: a model development and validation study.EClinicalMedicine · 2025Article
- Integrated Approach Using Intuitionistic Fuzzy Multicriteria Decision-Making to Support Classifier Selection for Technology Adoption in Patients with Parkinson Disease: Algorithm Development and Validation.JMIR rehabilitation and assistive technologies · 2024Article
- Multiple Imputation of Missing Race/Ethnicity Information in the National Assisted Reproductive Technology Surveillance System.Journal of women's health (2002) · 2024Article
- Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.Patterns (New York, N.Y.) · 2024Review
- Federated Multiple Imputation for Variables that Are Missing Not At Random in Distributed Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Elevated uric acid to serum albumin ratio: a predictor of short-term outcomes in Chinese heart failure patients.Frontiers in nutrition · 2024Article
- Article
- Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.medRxiv : the preprint server for health sciences · 2023Article
- The impact of imputation quality on machine learning classifiers for datasets with missing values.Communications medicine · 2023Article
- Association between mean arterial pressure and clinical outcomes among patients with heart failure.ESC heart failure · 2023Article
- Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?Journal of biomedical informatics · 2023Article
- Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study.Diagnostic and prognostic research · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Distributed health data networks (DHDNs) leverage data from multiple sources or sites such as electronic health records (EHRs) from multiple healthcare systems and have drawn increasing interests in recent years, as they do not require sharing of subject-level data and hence lower the hurdles for collaboration between institutions considerably. However, DHDNs face a number of challenges in data analysis, particularly in the presence of missing data. The current state-of-the-art methods for handling incomplete data require pooling data into a central repository before analysis, which is not feasible in DHDNs. In this paper, we address the missing data problem in distributed environments such as DHDNs that has not been investigated previously. We develop communication-efficient distributed multiple imputation methods for incomplete data that are horizontally partitioned. Since subject-level data are not shared or transferred outside of each site in the proposed methods, they enhance protection of patient privacy and have the potential to strengthen public trust in analysis of sensitive health data. We investigate, through extensive simulation studies, the performance of these methods. Our methods are applied to the analysis of an acute stroke dataset collected from multiple hospitals, mimicking a DHDN where health data are horizontally partitioned across hospitals and subject-level data cannot be shared or sent to a central data repository.
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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.