Predicting abatacept retention using machine learning.

Journal: Arthritis research & therapy
PMID: 39893489

Abstract

BACKGROUND: The incorporation of machine learning is becoming more prevalent in the clinical setting. By predicting clinical outcomes, machine learning can provide clinicians with a valuable tool for refining precision medicine approaches and improving treatment outcomes.

Authors

  • Rieke Alten
    Schlosspark-Klinik University, Berlin, Germany. Rieke.alten@schlosspark-klinik.de.
  • Claire Behar
    Tulsy, Paris, France.
  • Pierre Merckaert
    Data Revenue GmbH, Berlin, Germany.
  • Ebenezer Afari
    Private Practice, Lyon, France.
  • Virginie Vannier-Moreau
    Bristol Myers Squibb, Rueil-Malmaison, France.
  • Anael Ohayon
    Bristol Myers Squibb, Rueil-Malmaison, France.
  • Sean E Connolly
    Bristol Myers Squibb, Princeton, NJ, USA.
  • Aurélie Najm
    School of Infection and Immunity, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
  • Pierre-Antoine Juge
    Université de Paris, AP-HP, Hôpital Bichat Claude-Bernard, Paris, France.
  • Gengyuan Liu
    Bristol Myers Squibb, Princeton, NJ, USA.
  • Angshu Rai
    Digital Health & Innovation, Amgen Inc, Thousand Oaks, CA, United States.
  • Yedid Elbez
    Signifience, Puteaux, France.
  • Karissa Lozenski
    Bristol Myers Squibb, Princeton, NJ, USA.

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