On how to not misuse hierarchical clustering on principal components to define clinically meaningful patient subgroups. Response to: ‘On using machine learning algorithms to define clinical meaningful patient subgroups’ by Pinal-Fernandez and Mammen
Sign Up to like & getrecommendations! Published in 2019 at "Annals of the Rheumatic Diseases"
DOI: 10.1136/annrheumdis-2019-215868
Abstract: We thank Pinal-Fernandez and Mammen for their interesting methodological comment on our work in which we used hierarchical clustering on principal components to define clinically meaningful subgroups of patients with anti-Ku antibodies.1 2 We fully… read more here.
Keywords: clustering principal; patient subgroups; meaningful patient; hierarchical clustering ... See more keywords
The Application of Clustering on Principal Components for Nutritional Epidemiology: A Workflow to Derive Dietary Patterns
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DOI: 10.3390/nu15010195
Abstract: In the last decades, different multivariate techniques have been applied to multidimensional dietary datasets to identify meaningful patterns reflecting the dietary habits of populations. Among them, principal component analysis (PCA) and cluster analysis represent the… read more here.
Keywords: principal components; dietary data; components nutritional; clustering principal ... See more keywords