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Detection of atypical response trajectories in biomedical longitudinal databases
Pantazis, Lucio José García, Rafael Antonio
Pantazis, Lucio José
García, Rafael Antonio
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Artículo de publicación periódica
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Abstract
Many health care professionals and institutions manage longitudinal databases, involving follow-ups for different patients over time. Longitudinal data frequently manifest additional complexities such as high variability, correlated measurements and missing data. Mixed effects models have been widely used to overcome these difficulties. This work proposes the use of linear mixed effects models as a tool that allows to search conceptually different types of anomalies in the data simultaneously.
Description
Detection of atypical response trajectories in biomedical longitudinal databases
Date
2022-10-24
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De Gruyter
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Keywords
LONGITUDINAL DATA, MIXED EFFECTS MODELS, OUTLIER DETECTION, DATA LONGITUDINAL, MODELO DE EFECTOS MIXTOS