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

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....

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Bibliographic Details
Main Authors: Jip W. T. M. de Kok, Frank van Rosmalen, Jacqueline Koeze, Frederik Keus, Sander M. J. van Kuijk, José Castela Forte, Ronny M. Schnabel, Rob G. H. Driessen, Thijs T. W. van Herpt, Jan-Willem E. M. Sels, Dennis C. J. J. Bergmans, Chris P. H. Lexis, William P. T. M. van Doorn, Steven J. R. Meex, Minnan Xu, Xavier Borrat, Rachel Cavill, Iwan C. C. van der Horst, Bas C. T. van Bussel
Format: Article
Language:English
Published: Nature Portfolio 2024-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-51699-z

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