ArticleGigaScience2022
Benchmarking missing-values approaches for predictive models on health databases.
Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Nursing Process Data for Health Care Cost Prediction Using Machine Learning: Longitudinal Study.JMIR nursing · 2026Observational
- Testing Transferability of a Mortality Risk Model for Interhospital Transfer Using Real-World Electronic Health Records: External Validation and Model Enhancement.Critical care explorations · 2026Article
- A comparative study of quantum-inspired PSO and EA with their binary variants for heart disease classification.Scientific reports · 2026Article
- Development and validation of an online prognostic risk stratification tool to assist decisions on perioperative chemotherapy for men with luminal early breast cancer.Breast cancer research : BCR · 2026Article
- Longitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers.Scientific reports · 2026Article
- Benchmarking imputation strategies for missing time-series data in critical care using real-world-inspired scenarios.Scientific reports · 2026Article
- Comparison of missing data handling methods for variant pathogenicity predictors.NAR genomics and bioinformatics · 2025Article
- Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation.JMIR medical informatics · 2025Article
- Early Sepsis Prediction Using Publicly Available Data: High-Performance AI/ML Models with First-Hour Clinical Information.Diagnostics (Basel, Switzerland) · 2025Article
- Comparing the predictive performance of diabetes complications using administrative health data and clinical data.Scientific reports · 2025Article
- Clinical Context Is More Important than Data Quantity to the Performance of an Artificial Intelligence-Based Early Warning System.Journal of clinical medicine · 2025Article
- Article
- Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies.Diagnostics (Basel, Switzerland) · 2024Article
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Article
- Article
- Rescuing missing data in connectome-based predictive modeling.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Moving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records.Health data science · 2024Review
- The future of data analysis is now: Integrating generative AI in neuroimaging methods development.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review.BMC medical research methodology · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAs databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values. These large databases are well suited to train machine learning models, e.g., for forecasting or to extract biomarkers in biomedical settings. Such predictive approaches can use discriminative-rather than generative-modeling and thus open the door to new missing-values strategies. Yet existing empirical evaluations of strategies to handle missing values have focused on inferential statistics.
resultsHere we conduct a systematic benchmark of missing-values strategies in predictive models with a focus on large health databases: 4 electronic health record datasets, 1 population brain imaging database, 1 health survey, and 2 intensive care surveys. Using gradient-boosted trees, we compare native support for missing values with simple and state-of-the-art imputation prior to learning. We investigate prediction accuracy and computational time. For prediction after imputation, we find that adding an indicator to express which values have been imputed is important, suggesting that the data are missing not at random. Elaborate missing-values imputation can improve prediction compared to simple strategies but requires longer computational time on large data. Learning trees that model missing values-with missing incorporated attribute-leads to robust, fast, and well-performing predictive modeling.
conclusionsNative support for missing values in supervised machine learning predicts better than state-of-the-art imputation with much less computational cost. When using imputation, it is important to add indicator columns expressing which values have been imputed.
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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.