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Detection of atypical response trajectories in biomedical longitudinal databases
Lucio José Pantazis1,2, Rafael Antonio García1,2
1ITBA, Buenos Aires, Lavardén 315, CP 1437, Argentina.
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.
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