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

迁移学习对药物不良反应的早期预测 ： 多西他赛和脱发在乳腺癌中的个案研究。

• 发表时间: 2023 May 18
• 来源期刊: Stud Health Technol Inform
• DOI: 10.3233/SHTI230158
• PMID: 37203703
• 文章类型: Journal Article
• 作者列表: Dimitsaki S,Natsiavas P,Jaulent MC

关键词: Breast cancer; adverse drug reaction; alopecia; docetaxel; transfer learning

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

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.

摘要

迁移学习(TL)是一种尚未在医疗保健领域得到广泛研究的方法，主要应用于图像数据。本研究概述了利用个体病例安全报告（ICSR）和电子健康记录（EHR）的TL管道，用于早期检测药物不良反应（ADR），评估使用脱发和多西他赛对乳腺癌患者的使用情况。