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Detection of atypical response trajectories in biomedical longitudinal databases

Pantazis, Lucio José et al · De Gruyter · 2023

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Author / responsible party
Pantazis, Lucio José et al
Publisher
De Gruyter
Year
2023
ISSN
2020-0076
ISSN
2020-0076
Language
English

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Detection of atypical response trajectories in biomedical longitudinal databases 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. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)

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APA 7

Pantazis, L. J. E. A. (2023). Detection of atypical response trajectories in biomedical longitudinal databases. https://doi.org/10.1515/ijb-2020-0076

MLA

Pantazis, Lucio José et al. "Detection of atypical response trajectories in biomedical longitudinal databases." 2023. https://doi.org/10.1515/ijb-2020-0076.

Chicago

Pantazis, Lucio José et al. 2023. "Detection of atypical response trajectories in biomedical longitudinal databases.". https://doi.org/10.1515/ijb-2020-0076.

Harvard

Pantazis, L. J. E. A. 2023, Detection of atypical response trajectories in biomedical longitudinal databases, De Gruyter, available at: https://doi.org/10.1515/ijb-2020-0076 [Accessed 8 Aug. 2026].

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Title
Detection of atypical response trajectories in biomedical longitudinal databases
Author / contributors
Pantazis, Lucio José et al
Publisher
De Gruyter
Publication year
2023
ISSN
2020-0076
ISSN
2020-0076
Language
English

Subjects

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LONGITUDINAL DATA; MIXED EFFECTS MODELS; OUTLIER DETECTION; DATA LONGITUDINAL; MODELO DE EFECTOS MIXTOS
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