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Response to: ‘On using machine learning algorithms to define clinically meaningful patient subgroups’ by Pinal-Fernandez and Mammen

Authors

  • Olivier Benveniste 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 PubMed articlesGoogle scholar articles
  • Yves Allenbach 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 PubMed articlesGoogle scholar articles
  • Benjamin Granger Department of Biostatistics and Clinical Information, Sorbonne Université, INSERM UMR 1136, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France PubMed articlesGoogle scholar articles
  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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Citation

Benveniste O, Allenbach Y, Granger B
Response to: ‘On using machine learning algorithms to define clinically meaningful patient subgroups’ by Pinal-Fernandez and Mammen

Publication history

  • Received July 10, 2019
  • Revised July 10, 2019
  • Accepted July 12, 2019
  • First published July 20, 2019.

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