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Response to: ‘On using machine learning algorithms to define clinically meaningful patient subgroups’ by Pinal-Fernandez and Mammen
  1. Olivier Benveniste1,
  2. Yves Allenbach1,
  3. Benjamin Granger2
  1. 1 Department of Internal Medicine and Clinical Immunology and Paris Neuromuscular Rare Diseases Reference Center, Sorbonne Université, INSERM U974, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
  2. 2 Department of Biostatistics and Clinical Information, Sorbonne Université, INSERM UMR 1136, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
  1. Correspondence to Dr Olivier Benveniste, Department of Internal Medicine and Clinical Immunology, Hospital University Department: Inflammation, Immunopathology and Biotherapy (DHU i2B), Assistance Publique - Hôpitaux de Paris, Pitié-Salpêtrière University Hospital, Paris 75013, France; olivier.benveniste{at}aphp.fr

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We have read with interest the comment from Pinal-Fernandez and Mammen in which they question the statistical clustering methods based on unsupervised learning analyses to define clinically meaningful patient subgroups.1 Pinal-Fernandez and Mammen base their arguments on the production of an analysis according to this methodology made on a random simulated data set that would highlight the formation of three clusters, in fact arbitrary.

It is important to point out that the example which forms the basis of their argument is ill-chosen because it shows a misguided use of this type of technique. Indeed, before applying a clustering method …

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