Application to pathways of elderly patients after hospitalization. The DAMAGE study.

We introduce unsupervised and supervised models for categorical functional data. Multiple correspondance analysis is extended to functional categorical data and principal components are used as latent variables for clustering and regression models. An application on DAMAGE medical dataset is presented.

Publication Details

Journal
LillOA (Université de Lille (University Of Lille))
Published
2024-10-15
Primary Topic
Machine Learning in Healthcare
Type
article
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article

Application to pathways of elderly patients after hospitalization. The DAMAGE study.

LillOA (Université de Lille (University Of Lille))
Machine Learning in Healthcare
article

Application to pathways of elderly patients after hospitalization. The DAMAGE study.

article en

Abstract

We introduce unsupervised and supervised models for categorical functional data. Multiple correspondance analysis is extended to functional categorical data and principal components are used as latent variables for clustering and regression models. An application on DAMAGE medical dataset is presented.

LillOA (Université de Lille (University Of Lille))
Openalex Percentile: Top 56%
Machine Learning in Healthcare
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Application to pathways of elderly patients after hospitalization. The DAMAGE study. · LillOA (Université de Lille (University Of Lille)) (2024) | TGRS Research Map | TGRS