Open AccessAutomated Identification of Patients With Immune-Related Adverse Events From Clinical Notes Using Word Embedding and Machine Learning
Author(s) -
Samir Gupta,
Anas Belouali,
Neil Shah,
Michael B. Atkins,
Subha Madhavan
Publication year - 2021
Publication title -
jco clinical cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 12
ISSN - 2473-4276
DOI - 10.1200/cci.20.00109
Subject(s) - computer science , artificial intelligence , word embedding , machine learning , natural language processing , adverse effect , receiver operating characteristic , classifier (uml) , support vector machine , f1 score , health records , identification (biology) , medicine , embedding , health care , economics , economic growth , botany , biology
Although immune checkpoint inhibitors (ICIs) have substantially improved survival in patients with advanced malignancies, they are associated with a unique spectrum of side effects termed immune-related adverse events (irAEs). To ensure treatment safety, research efforts are needed to comprehensively detect and understand irAEs. Retrospective analysis of data from electronic health records can provide knowledge to characterize these toxicities. However, such information is not captured in a structured format within the electronic health record and requires manual chart review.