Individualized Treatment Effect Prediction with Machine Learning - Salient Considerations.

Journal: NEJM evidence
PMID: 38776640

Abstract

BACKGROUND: Machine learning-based approaches that seek to accomplish individualized treatment effect prediction have gained traction; however, some salient challenges lack wider recognition.

Authors

  • Rishi J Desai
    Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Robert J Glynn
    Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston.
  • Scott D Solomon
    Brigham and Women's Hospital, Boston, MA, USA.
  • Brian Claggett
    Brigham and Women's Hospital, Boston, MA, USA.
  • Shirley V Wang
    Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont St Suite 303, Boston, MA, 02120, USA. swang1@bwh.harvard.edu.
  • Muthiah Vaduganathan
    Brigham and Women's Hospital Heart and Vascular Center, Department of Medicine, Harvard Medical School, Boston, MA.

Keywords

External Resources

Popular Topics
Recent Journals