AUTHOR=Rathee Sanjay , MacMahon Meabh , Liu Anika , Katritsis Nicholas M. , Youssef Gehad , Hwang Woochang , Wollman Lilly , Han Namshik 

TITLE=DILIC: An AI-Based Classifier to Search for Drug-Induced Liver Injury Literature

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.867946

DOI=10.3389/fgene.2022.867946

ISSN=1664-8021

ABSTRACT=Drug-Induced Liver Injury (DILI) is a class of Adverse Drug Reaction (ADR) which causes problems in both clinical and research settings. It is the most frequent cause of acute liver failure in the majority of western countries and is a major cause of attrition of novel drug candidates. Manual trawling of literature for is the main route of deriving information on DILI from research studies. This makes it an inefficient process prone to human error. Therefore, an automatized AI model capable of retrieving DILI-related papers from the huge ocean of literature could be invaluable for the drug discovery community.
In this project, we built an artificial intelligence (AI) model combining the power of Natural Language Processing (NLP) and Machine Learning (ML) to address this problem. This model uses NLP to filter out meaningless text (e.g. stopwords) and uses customized functions to extract relevant keywords as singleton, pair, triplet and so on. These keywords are processed by apriori pattern mining algorithm to extract relevant patterns which are used to estimate initial weightings for a ML classifier. Along with pattern importance and frequency, an FDA-approved drug list mentioning DILI adds extra confidence in classification. The combined power of these methods build a DILI classifier (DILI$_C$) with 94.91% cross-validation and 94.14% external validation accuracy.