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- Title
Deep embedded clustering generalisability and adaptation for integrating mixed datatypes: two critical care cohorts.
- Authors
de Kok, Jip W. T. M.; van Rosmalen, Frank; Koeze, Jacqueline; Keus, Frederik; van Kuijk, Sander M. J.; Castela Forte, José; Schnabel, Ronny M.; Driessen, Rob G. H.; van Herpt, Thijs T. W.; Sels, Jan-Willem E. M.; Bergmans, Dennis C. J. J.; Lexis, Chris P. H.; van Doorn, William P. T. M.; Meex, Steven J. R.; Xu, Minnan; Borrat, Xavier; Cavill, Rachel; van der Horst, Iwan C. C.; van Bussel, Bas C. T.
- 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 models has insufficiently been validated on external populations, we assessed external validity by investigating cluster generalisability onto an external validation dataset. We concluded that both DEC and X-DEC resulted in clinically recognisable and generalisable clusters, but X-DEC produced much more stable clusters.
- Subjects
CRITICAL care medicine; INTENSIVE care patients; PHYSIOLOGICAL adaptation
- Publication
Scientific Reports, 2024, Vol 14, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-024-51699-z