Observational studyBMC medical research methodology2021
Generative adversarial networks for imputing missing data for big data clinical research.
Observational study in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03299010 (10-year Risk Prediction Models of Complications and Mortality of Diabetes Mellitus in Chinese Patients in Primary Care in Hong Kong), which is not on this map. Cited by 21 papers, 2 of them syntheses that pooled it.
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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.
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.
10-year Risk Prediction Models of Complications and Mortality of Diabetes Mellitus in Chinese Patients in Primary Care in Hong Kong
Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Missing data handling in pediatric appendectomy research: current practices and National Surgical Quality Improvement Program (NSQIP) analysis.Surgical endoscopy · 2026Pooled it
- Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review.BMC medical research methodology · 2024Pooled it
- Machine learning for missing data imputation in Alzheimer's research: predicting medial temporal lobe dynamic flexibility.Cognitive neurodynamics · 2026Article
- Predicting sentinel lymph node metastasis in breast cancer using an interpretable machine learning approach based on multi-domain clinical features.Breast cancer research : BCR · 2026Article
- The future of power forecasting: neuromorphic-axolotl hybrid intelligence revolutionizing grid operations through bio-inspired missing data mastery.Scientific reports · 2026Article
- Machine Learning-Based Prediction of In-Hospital Falls in Adult Inpatients: Retrospective Observational Multicenter Study.JMIR medical informatics · 2025Observational
- Clustering-Informed Shared-Structure Variational Autoencoder for Missing Data Imputation in Large-Scale Healthcare Data.Statistics in medicine · 2025Article
- Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues.CPT: pharmacometrics & systems pharmacology · 2025Review
- SMART: Structured Missingness Analysis and Reconstruction Technique for credit scoring.Scientific reports · 2025Article
- Conceptual framework as a guide to choose appropriate imputation method for missing values in a clinical structured dataset.BMC medical research methodology · 2025Article
- Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.Nature communications · 2025Article
- Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods.Sensors (Basel, Switzerland) · 2025Article
- Development and validation of HBV surveillance models using big data and machine learning.Annals of medicine · 2024Article
- A novel MissForest-based missing values imputation approach with recursive feature elimination in medical applications.BMC medical research methodology · 2024Article
- Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network.Sensors (Basel, Switzerland) · 2024Article
- Approaching a nationwide registry: analyzing big data in patients with heart failure.Turkish journal of medical sciences · 2024Article
- Machine learning-based remission prediction in rheumatoid arthritis patients treated with biologic disease-modifying anti-rheumatic drugs: findings from the Kuwait rheumatic disease registry.Frontiers in big data · 2024Article
- Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management.Journal of anesthesia, analgesia and critical care · 2023Article
- A simulation study on missing data imputation for dichotomous variables using statistical and machine learning methods.Scientific reports · 2023Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMissing data is a pervasive problem in clinical research. Generative adversarial imputation nets (GAIN), a novel machine learning data imputation approach, has the potential to substitute missing data accurately and efficiently but has not yet been evaluated in empirical big clinical datasets.
objectivesThis study aimed to evaluate the accuracy of GAIN in imputing missing values in large real-world clinical datasets with mixed-type variables. The computation efficiency of GAIN was also evaluated. The performance of GAIN was compared with other commonly used methods, MICE and missForest.
methodsTwo real world clinical datasets were used. The first was that of a cohort study on the long-term outcomes of patients with diabetes (50,000 complete cases), and the second was of a cohort study on the effectiveness of a risk assessment and management programme for patients with hypertension (10,000 complete cases). Missing data (missing at random) to independent variables were simulated at different missingness rates (20, 50%). The normalized root mean square error (NRMSE) between imputed values and real values for continuous variables and the proportion of falsely classified (PFC) for categorical variables were used to measure imputation accuracy. Computation time per imputation for each method was recorded. The differences in accuracy of different imputation methods were compared using ANOVA or non-parametric test.
resultsBoth missForest and GAIN were more accurate than MICE. GAIN showed similar accuracy as missForest when the simulated missingness rate was 20%, but was more accurate when the simulated missingness rate was 50%. GAIN was the most accurate for the imputation of skewed continuous and imbalanced categorical variables at both missingness rates. GAIN had a much higher computation speed (32 min on PC) comparing to that of missForest (1300 min) when the sample size is 50,000.
conclusionGAIN showed better accuracy as an imputation method for missing data in large real-world clinical datasets compared to MICE and missForest, and was more resistant to high missingness rate (50%). The high computation speed is an added advantage of GAIN in big clinical data research. It holds potential as an accurate and efficient method for missing data imputation in future big data clinical research.
trial registrationClinicalTrials.gov ID: NCT03299010 ; Unique Protocol ID: HKUCTR-2232.
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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.