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Response to ‘Augmented vs. artificial intelligence for stratification of patients with myositis’ by Mahler et al
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  1. Alain Meyer1,2,
  2. Lionel Spielmann3,
  3. François Séverac4,5
  1. 1 Exploration Fonctionnelle Musculaire, Service de physiologie, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
  2. 2 Centre National de Référence des Maladies Auto-Immunes Systémiques Rares de l'Est et du Sud-Ouest, Service de rhumatologie, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
  3. 3 Service de Rhumatologie, Hôpitaux civils de Colmar, Colmar, France
  4. 4 Service de Santé Publique, GMRC, CHU de Strasbourg, Strasbourg, France
  5. 5 ICube, UMR 7357, équipe IMAGeS, Université de Strasbourg, Strasbourg, France
  1. Correspondence to Dr Lionel Spielmann, Service de Rhumatologie, Hospices civils de Colmar, Colmar, Alsace 68024, France; lionel.spielmann{at}ch-colmar.fr

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We thank Mahler et al for their comment1 on our work in which we used hierarchical clustering on principal components to define clinically meaningful subgroups of patients with anti-Ku antibodies.2

Mahler et al argue for the use of machine learning alongside expert decision, thus relying on augmented judgement in making the final decision on patient stratification. We share this view.

In this regard, we disagree with the statement according to which the hierarchical clustering on principal components applied to 1000 observations with a multivariate normal distribution proposed by Pinal-Fernandez and Mammen3 and the hierarchical clustering on principal components applied to our observations from 42 anti-Ku patients yielded similar results.

As Mahler et al stated, clustering …

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