Article
Health Care Sciences & Services
Jiang Li, Xiaowei S. Yan, Durgesh Chaudhary, Venkatesh Avula, Satish Mudiganti, Hannah Husby, Shima Shahjouei, Ardavan Afshar, Walter F. Stewart, Mohammed Yeasin, Ramin Zand, Vida Abedi
Summary: Laboratory data from EHR can be used in prediction models to mitigate estimation bias and improve model performance with missingness using imputation methods. The study found that missingness in EHR laboratory variables was associated with patients' comorbidity data, and the multi-level imputation algorithm showed smaller imputation error compared to the cross-sectional method.
NPJ DIGITAL MEDICINE
(2021)
Article
Health Care Sciences & Services
Lauren J. Beesley, Irina Bondarenko, Michael R. Elliot, Allison W. Kurian, Steven J. Katz, Jeremy M. G. Taylor
Summary: This paper describes how to generalize the sequential regression multiple imputation procedure to handle non-random missingness when missingness may depend on other variables. The method reduces bias in the final analysis compared to standard techniques, using approximation strategies involving inclusion of an offset in the imputation model.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2021)
Article
Multidisciplinary Sciences
Faisal Maqbool Zahid, Shahla Faisal, Christian Heumann
Summary: In high-dimensional settings, Multiple Imputation (MI) is challenging, a semi-compatible imputation model is proposed by relaxing the lasso penalty and using a ridge penalty to address instability and convergence issues. The proposed approach shows superior performance to existing MI techniques in simulation studies and real-life datasets while addressing compatibility problems.
Article
Urology & Nephrology
Katrina Blazek, Anita van Zwieten, Valeria Saglimbene, Armando Teixeira-Pinto
Summary: Health data often have missing values, and utilizing multiple imputation techniques can help reduce bias and maintain sample size. Correct specification of the imputation model is crucial for the validity of analyses. Considerations such as missing mechanism, imputation method, and result reporting are important when conducting research with multiply imputed data.
KIDNEY INTERNATIONAL
(2021)
Article
Computer Science, Artificial Intelligence
Xu Zhou, Xiaofeng Liu, Gongjin Lan, Jian Wu
Summary: Air quality is a major global concern, leading to the deployment of intelligent monitoring networks in various places. However, these networks often have missing data, posing challenges for air quality studies. The use of generative adversarial networks (GAN) has shown promising results for data imputation in air quality monitoring, especially when combining data from different owners without sharing detailed information. Results from a federated GAN method show improved model performance and stability across collaborating participants.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Feng Zhao, Yan Lu, Xinning Li, Lina Wang, Yingjie Song, Deming Fan, Caiming Zhang, Xiaobo Chen
Summary: Credit risk assessment is crucial for banks in loan approval and risk management. However, missing credit risk data can significantly reduce the effectiveness of the assessment model. In this paper, a novel method named MGAIN is proposed to accurately predict missing data through subset selection and multiple imputation strategy, improving the accuracy of the imputation model.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Gayathri Nagarajan, L. D. Dhinesh Babu
Summary: A novel approach to missing data imputation in biomedical datasets using an ensemble of deeply learned clustering and L2 regularized regression based on symmetric uncertainty is proposed. Experimental results show that the proposed approach outperforms other methods in terms of imputation accuracy and computational efficiency while preserving the dataset structure.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Mathematics
Fangfang Li, Hui Sun, Yu Gu, Ge Yu
Summary: This paper proposes a noise-aware missing data multiple imputation algorithm NPMI for static data. Different multiple imputation models are proposed according to the missing mechanism of data. The method to determine the imputation order of multivariablesmissing is given. Experiments on real and synthetic datasets verify the accuracy and efficiency of the proposed algorithm.
Article
Engineering, Multidisciplinary
Han Honggui, Sun Meiting, Wu Xiaolong, Li Fangyu
Summary: This article proposes a double-cycle weighted imputation (DCWI) method to deal with multiple missing patterns in the wastewater treatment process. The method maximizes the utilization of available information to improve imputation accuracy and experimental results show its superiority over comparison methods.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Jingmao Li, Qingzhao Zhang, Song Chen, Kuangnan Fang
Summary: In this article, a novel weighted multiple blockwise imputation method is proposed to address the problem of high-dimensional regression with blockwise missing data. The method demonstrates superior performance in variable selection, parameter estimation, and prediction ability.
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Yan Xia, Le Zhang, Nishant Ravikumar, Rahman Attar, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi
Summary: A new robust approach called Image Imputation Generative Adversarial Network (I2-GAN) is proposed to learn and infer missing slices in cardiac magnetic resonance sequences, improving accuracy of cardiac volume measurements. Experimental results show significant improvements in missing slice imputation for CMR using this method.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Multidisciplinary Sciences
Hannah Voss, Simon Schlumbohm, Philip Barwikowski, Marcus Wurlitzer, Matthias Dottermusch, Philipp Neumann, Hartmut Schlueter, Julia E. Neumann, Christoph Krisp
Summary: HarmonizR is an efficient tool for missing data tolerant experimental variance reduction, which does not require data imputation and can be easily adjusted for individual dataset properties and user preferences. It demonstrated successful data harmonization for different tissue preservation techniques, LC-MS/MS instrumentation setups, and quantification approaches, and outperformed data imputation methods in detecting significant proteins.
NATURE COMMUNICATIONS
(2022)
Article
Engineering, Civil
Jiping Xing, Ronghui Liu, Khadka Anish, Zhiyuan Liu
Summary: In this paper, a tensor decomposition framework called data fusion CANDECOMP/PARAFAC (DFCP) is proposed to combine vehicle license plate recognition (LPR) data and cellphone location (CL) data for interval-wise missing volume imputation on urban networks. Numerical experiments show that our proposed method significantly outperforms the imputation method using LPR data only, and a sensitivity analysis demonstrates the robustness of the model performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Health Care Sciences & Services
Martijn W. Heymans, Jos W. R. Twisk
Summary: Proper handling of missing data is crucial, and consideration should be given to the mechanism of missing data. Multiple imputations are highly recommended for estimating missing values. It is important to prevent missing data rather than treating them.
JOURNAL OF CLINICAL EPIDEMIOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Siddharth Ramchandran, Gleb Tikhonov, Otto Lonnroth, Pekka Tiikkainen, Harri Lahdesmaki
Summary: Conditional variational autoencoders (CVAEs) are versatile deep latent variable models that extend the standard VAE framework by conditioning the generative model with auxiliary covariates. This paper proposes a method to learn conditional VAEs from datasets with missing values in auxiliary covariates, and demonstrates superior performance compared to previous methods in various experimental settings.
PATTERN RECOGNITION
(2024)