A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative

[Display omitted] 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...

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Veröffentlicht in:	Journal of biomedical informatics 2023-03, Vol.139, p.104295-104295, Article 104295
Hauptverfasser:	Casiraghi, Elena, Wong, Rachel, Hall, Margaret, Coleman, Ben, Notaro, Marco, Evans, Michael D., Tronieri, Jena S., Blau, Hannah, Laraway, Bryan, Callahan, Tiffany J., Chan, Lauren E., Bramante, Carolyn T., Buse, John B., Moffitt, Richard A., Stürmer, Til, Johnson, Steven G., Raymond Shao, Yu, Reese, Justin, Robinson, Peter N., Paccanaro, Alberto, Valentini, Giorgio, Huling, Jared D., Wilkins, Kenneth J.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Bias Clinical informatics COVID-19 COVID-19 severity assessment Diabetic patients Evaluation framework Humans Multiple Imputation Probability Research Design
Online-Zugang:	Volltext
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