ArticleNPJ digital medicine2021
Imputation of missing values for electronic health record laboratory data.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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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
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence-Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Benchmarking imputation methods on real-world clinical time series with simulated spatio-temporal missingness.Nature communications · 2026Article
- Leveraging Ensemble Machine Learning Models for the Detection of Primary Myelofibrosis in Electronic Health Records.Cancers · 2026Article
- External validation of the SMART2 model for recurrent cardiovascular risk: performance in socioeconomic, ethnic and psychiatric subgroups.Open heart · 2026Article
- Machine Learning for Early Detection and Prevention of Disease Using Electronic Health Records.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Clustering-Informed Shared-Structure Variational Autoencoder for Missing Data Imputation in Large-Scale Healthcare Data.Statistics in medicine · 2025Article
- Compact machine learning model for perioperative stroke prediction prior to surgery: A retrospective cohort study.Scientific reports · 2025Article
- Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation.JMIR medical informatics · 2025Article
- Incompleteness of Electronic Health Records: An Impending Process Problem Within Healthcare.Healthcare (Basel, Switzerland) · 2025Article
- Prediction of postoperative infections by strategic data imputation and explainable machine learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Wearable device-measured circadian rest-activity rhythm with mortality risk in patients with cancer.BMJ health & care informatics · 2025Article
- A self-supervised framework for laboratory data imputation in electronic health records.Communications medicine · 2025Article
- A probabilistic approach for building disease phenotypes across electronic health records.BioData mining · 2025Article
- Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models.JAMA network open · 2025Article
- Flexible imputation toolkit for electronic health records.Scientific reports · 2025Article
- Computational Phenomapping of Randomized Clinical Trial Participants to Enable Assessment of Their Real-World Representativeness and Personalized Inference.Circulation. Cardiovascular quality and outcomes · 2025Article
- Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study.Intensive care medicine experimental · 2025Article
- The health-related quality of life among survivors with post-COVID conditions in the United States.PloS one · 2025Article
- Five-year dementia prediction and decision support system based on real-world data.Frontiers in aging neuroscience · 2025Article
- An open-source framework for end-to-end analysis of electronic health record data.Nature medicine · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods. We demonstrate the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health to: (1) characterize the patterns of missingness in laboratory variables; (2) simulate two missing mechanisms, arbitrary and monotone; (3) compare cross-sectional and multi-level multivariate missing imputation algorithms applied to laboratory data; (4) assess whether incorporation of latent information, derived from comorbidity data, can improve the performance of the algorithms. The latter was based on a case study of hemoglobin A1c under a univariate missing imputation framework. Overall, the pattern of missingness in EHR laboratory variables was not at random and was highly associated with patients' comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method.
Identifiers
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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.