Model and Strategy for Predicting and Discovering Drug-Drug Interactions.

Journal: Studies in health technology and informatics
Published Date:

Abstract

Taking several medications at the same time is an increasingly common phenomenon in our society. The combination of drugs is certainly not without risk of potentially dangerous interactions. Taking into account all possible interactions is a very complex task as it is not yet known what all possible interactions between drugs and their types are. Machine learning based models have been developed to help with this task. However, the output of these models is not structured enough to be integrated in a clinical reasoning process on interactions. In this work, we propose a clinically relevant and technically feasible model and strategy for drug interactions.

Authors

  • Abdelmalek Mouazer
    Université Sorbonne Paris Nord, LIMICS, INSERM, F-93000, Bobigny, France.
  • Nada Boudegzdame
    Université Sorbonne Paris Nord, LIMICS, INSERM, F-93000, Bobigny, France.
  • Karima Sedki
    Sorbonne Universités, UPMC Univ Paris 06, INSERM, Sorbonne Paris Cité, Université Paris 13, LIMICS, UMR_S 1142, Paris, France.
  • Rosy Tsopra
    LIMICS, INSERM UMRS 1142, Université Paris 13, Sorbonne Paris Cité, 93017 Bobigny, France UPMC Université Paris 6, Sorbonne Universités, Paris.
  • Jean-Baptiste Lamy
    LIMICS, Université Paris 13, Sorbonne Paris Cité, 93017 Bobigny, France.