| Literature DB >> 32570592 |
Raphaël Chauvet1,2, Cédric Bousquet1, Agnès Lillo-Lelouet3, Ilan Zana1, Ilan Ben Kimoun1, Marie-Christine Jaulent1.
Abstract
This poster presents a non-exhaustive study of machine learning classification algorithms on pharmacovigilance data. In this study, we have taken into account the patient's clinical data such as medical history, medications taken and their indications for prescriptions, and the observed side effects. From these elements we determine whether the patient case is considered serious or not. We show the performances of the different algorithms by their precision, recall and accuracy as well as their learning curves.Entities:
Keywords: Classification; Decision support tool; Machine Learning; Pharmacovigilance
Year: 2020 PMID: 32570592 DOI: 10.3233/SHTI200375
Source DB: PubMed Journal: Stud Health Technol Inform ISSN: 0926-9630