Transfer Learning for Early Prediction of Adverse Drug Reactions: Docetaxel and Alopecia in Breast Cancer as a Case Study

Stella Dimitsaki; Pantelis Natsiavas; Marie-Christine Jaulent

doi:10.3233/SHTI230158

Transfer Learning for Early Prediction of Adverse Drug Reactions: Docetaxel and Alopecia in Breast Cancer as a Case Study

Stud Health Technol Inform. 2023 May 18:302:396-397. doi: 10.3233/SHTI230158.

Authors

Stella Dimitsaki^{1

2}, Pantelis Natsiavas², Marie-Christine Jaulent¹

Affiliations

¹ Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la eSanté, LIMICS, F-75006 Paris, France.
² Institute of Applied Biosciences, Centre for Research and Development Hellas, Thessaloniki, Greece.

PMID: 37203703
DOI: 10.3233/SHTI230158

Abstract

Transfer Learning (TL) is an approach which has not yet been widely investigated in healthcare, mostly applied in image data. This study outlines a TL pipeline leveraging Individual Case Safety reports (ICSRs) and Electronic Health Records (EHR), applied for the early detection Adverse Drug Reactions (ADR), evaluated using of alopecia and docetaxel on breast cancer patients as use case.

Keywords: Breast cancer; adverse drug reaction; alopecia; docetaxel; transfer learning.

MeSH terms

Adverse Drug Reaction Reporting Systems
Alopecia / chemically induced
Breast Neoplasms* / drug therapy
Docetaxel / adverse effects
Drug-Related Side Effects and Adverse Reactions* / diagnosis
Female
Humans
Machine Learning

Substances

Docetaxel