Machine learning (ML) methodology used in the social and health sciences needs to fit the
intended research purposes of description, prediction, or causal inference. This paper …

Proteomics data are often plagued with missingness issues. These missing values (MVs)
threaten the integrity of subsequent statistical analyses by reduction of statistical power …

Background In many countries, the prevalence of non-communicable diseases risk factors is
commonly assessed through self-reported information from health interview surveys. It has …

Missing data pose significant challenges during representation learning of observational
data. The incompleteness of data can result in a deterioration of generative performance in …

Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity. Predicting the
associated risks of patients with chest pain using electronic health record data can help …

Multiple imputation techniques are commonly used when data are missing, however, there
are many options one can consider. Multivariate imputation by chained equations is a …

Railway accident causation analysis is fundamental to understanding the nature of railway
safety. Although a considerable number of prior studies have investigated this context, many …

岩爆是深部资源开采过程中亟待解决的问题之一. 为安全高效地预测岩爆灾害,
提出一种基于链式方程多重插补法(MICE) 与卷积神经网络(CNN) 的岩爆烈度等级预测模型 …

Imputation of missing data in high-dimensional datasets with more variables P than samples
N, P≫ N, is hampered by the data dimensionality. For multivariate imputation, the …

Background Micro-randomized trials (MRTs) enhance the effects of mHealth by determining
the optimal components, timings, and frequency of interventions. Appropriate handling of …