.png){kind=icon}
.png){kind=icon}
Return
A method for comparing multiple imputation techniques: A case study on the US national COVID cohort collaborative
.png){kind=icon}
OA
AI
DOI：10.1016/j.jbi.2023.104295.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging.
Keywords:
Multiple Imputation
Evaluation framework
Clinical informatics
Diabetic patients
COVID-19 severity assessment
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.5
Papers:
3.5K
Citations:
1.9W
Organization
View more organizations