Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study

JMIR Medical Informatics
Yue YuGuoqian Jiang

Abstract

Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration-approved immune checkpoint inhibitors. In our framework, we first used the Food and Drug Administration's Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categ...Continue Reading

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Citations

Apr 25, 2020·European Journal of Epidemiology·Sarah BauermeisterJohn E J Gallacher
Jun 27, 2020·Journal of the American Geriatrics Society·Julie P W BynumV G Vinod Vydiswaran
Sep 8, 2021·Frontiers in Cell and Developmental Biology·Silvia ParoloRosario Lombardo

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Software Mentioned

DailyMed
AEOLUS
cTAKES
OHDSI
MedDRA
pgAdmin
ADEpedia
cTAKES )
on
FAERS

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