Assessing the Impact of Imputation on the Interpretations of Prediction Models: A Case Study on Mortality Prediction for Patients with Acute Myocardial Infarction.

Autor:	Payrovnaziri SN; Florida State University, Tallahassee, Florida, USA., Xing A; Florida State University, Tallahassee, Florida, USA., Salman S; Florida State University, Tallahassee, Florida, USA., Liu X; Florida State University, Tallahassee, Florida, USA., Bian J; University of Florida, Gainesville, Florida, USA., He Z; Florida State University, Tallahassee, Florida, USA.
Jazyk:	angličtina
Zdroj:	AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science [AMIA Jt Summits Transl Sci Proc] 2021 May 17; Vol. 2021, pp. 465-474. Date of Electronic Publication: 2021 May 17 (Print Publication: 2021).
Abstrakt:	Acute myocardial infarction poses significant health risks and financial burden on healthcare and families. Prediction of mortality risk among AM! patients using rich electronic health record (EHR) data can potentially save lives and healthcare costs. Nevertheless, EHR-based prediction models usually use a missing data imputation method without considering its impact on the performance and interpretability of the model, hampering its real-world applicability in the healthcare setting. This study examines the impact of different methods for imputing missing values in EHR data on both the performance and the interpretations of predictive models. Our results showed that a small standard deviation in root mean squared error across different runs of an imputation method does not necessarily imply a small standard deviation in the prediction models' performance and interpretation. We also showed that the level of missingness and the imputation method used can have a significant impact on the interpretation of the models. (©2021 AMIA - All rights reserved.)
Databáze:	MEDLINE
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