Deep imputation of missing values in time series health data: A review with benchmarking. [PDF]
J Biomed Inform, 2023 The imputation of missing values in multivariate time series (MTS) data is a critical step in ensuring data quality and producing reliable data-driven predictive models.
Kazijevs M, Samad MD.
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The impact of imputation quality on machine learning classifiers for datasets with missing values. [PDF]
Commun Med (Lond), 2023 Background Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods ...
Shadbahr T +18 more
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Optimal clustering with missing values [PDF]
BMC Bioinformatics, 2019 Background Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements.
Shahin Boluki +3 more
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Benchmarking missing-values approaches for predictive models on health databases. [PDF]
Gigascience, 2022 Background As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values. These large databases are well suited to train machine learning models, e.g., for forecasting or to extract biomarkers
Perez-Lebel A +4 more
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A note on handling conditional missing values [PDF]
Global EpidemiologyIn medical research, some variables are conditionally defined on some levels of another variable, leading to conditional missing data. Imputation of this type of structural missing data is needed given inefficiency of listwise deletion inherent in ...
Mohammad Ali Mansournia +2 more
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A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records. [PDF]
Entropy (Basel), 2022 Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing.
Batra S +5 more
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Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review. [PDF]
BMC Med Res MethodolComprehending the research dataset is crucial for obtaining reliable and valid outcomes. Health analysts must have a deep comprehension of the data being analyzed.
Afkanpour M, Hosseinzadeh E, Tabesh H.
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Imputation of missing values for electronic health record laboratory data. [PDF]
NPJ Digit Med, 2021 Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods.
Li J +11 more
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Capturing Missing Tuples and Missing Values [PDF]
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2010 Databases in real life are often neither entirely closed-world nor entirely open-world. Indeed, databases in an enterprise are typically partially closed, in which a part of the data is constrained by master data that contains complete information about the enter prise in certain aspects 121].
Fan, Wenfei, Geerts, Floris
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missMDA: A Package for Handling Missing Values in Multivariate Data Analysis
Journal of Statistical Software, 2016 We present the R package missMDA which performs principal component methods on incomplete data sets, aiming to obtain scores, loadings and graphical representations despite missing values.
Julie Josse, François Husson
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