Deep imputation of missing values in time series health data: A review with benchmarking

The imputation of missing values in multivariate time series (MTS) data is critical in ensuring data quality and producing reliable data-driven predictive models. Apart from many statistical approaches, a few recent studies have proposed state-of-the-art deep learning methods to impute missing value...

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Veröffentlicht in:	Journal of biomedical informatics 2023-08, Vol.144, p.104440-104440, Article 104440
Hauptverfasser:	Kazijevs, Maksims, Samad, Manar D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Benchmarking Cross-sectional imputation Cross-Sectional Studies Deep neural network Electronic health records Longitudinal imputation Missing value imputation Multivariate data Research Design Sensor data Surveys and Questionnaires Time Factors Time series
Online-Zugang:	Volltext
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