Deep embedded clustering generalisability and adaptation for integrating mixed datatypes: two critical care cohorts.

ABSTRACT: We validated a Deep Embedded Clustering (DEC) model and its adaptation for integrating mixed datatypes (in this study, numerical and categorical variables). Deep Embedded Clustering (DEC) is a promising technique capable of managing extensive sets of variables and non-linear relationships. Nevertheless, DEC cannot adequately handle mixed datatypes. Therefore, we adapted DEC by replacing the autoencoder with an X-shaped variational autoencoder (XVAE) and optimising hyperparameters for cluster stability. We call this model "X-DEC". We compared DEC and X-DEC by reproducing a previous study that used DEC to identify clusters in a population of intensive care patients. We assessed internal validity based on cluster stability on the development dataset. Since generalisability of clustering model

SUBMITTER: de Kok JWTM 

PROVIDER: S-EPMC10781731 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature