ArticleScientific reports2025
Flexible imputation toolkit for electronic health records.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- CleanSurvival: automated data preprocessing for time-to-event models using reinforcement learning.BMC medical informatics and decision making · 2026Article
- A Heterogeneous Graph Neural Network Framework for Multi-Horizon Stroke Mortality Prediction.medRxiv : the preprint server for health sciences · 2026Article
- Findings from a transformer-based prediction model: social norms and confidentiality are associated with STIs risk in British men and women.BMC public health · 2026Article
- Machine Learning-Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2- Early Breast Cancer Using Real-World and NATALEE Data.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Machine learning models for predicting new-onset diabetes following acute pancreatitis using real-world data.BMJ public health · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Missing data in electronic health records (EHRs) poses a significant challenge for analysis. This study introduces Pympute, a comprehensive Python package designed for efficient and robust missing value imputation for EHRs. Pympute's core algorithm, Flexible, intelligently selects the optimal imputation method for each variable based on its characteristics. Pympute offers a comprehensive suite of functionalities. It benchmarks the performance of ten existing machine learning imputation algorithms against Flexible on real-world EHR datasets containing laboratory measurements. Additionally, Pympute facilitates data simulation, generating realistic datasets mimicking real-world data distributions for controlled evaluation of imputation performance. Finally, Pympute investigates how missingness and skewness, influence the selection of optimal imputation algorithms within the Flexible framework. Our findings validate that Pympute's Flexible method significantly improves imputation performance compared to the single model approach. Notably, simulating data solely based on covariance does not accurately reflect real-world selection behavior. Furthermore, skewness in the data distribution prompts Flexible to favor nonlinear imputation models. This study highlights the importance of considering data distribution patterns when selecting imputation algorithms. Pympute addresses this challenge by offering a versatile and user-friendly solution for diverse EHR data scenarios.
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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.