Improved prediction of drug-induced liver injury literature using natural language processing and machine learning methods Journal Article
| Authors: | Oh, J. H.; Tannenbaum, A.; Deasy, J. O. |
| Article Title: | Improved prediction of drug-induced liver injury literature using natural language processing and machine learning methods |
| Abstract: | Drug-induced liver injury (DILI) is an adverse hepatic drug reaction that can potentially lead to life-threatening liver failure. Previously published work in the scientific literature on DILI has provided valuable insights for the understanding of hepatotoxicity as well as drug development. However, the manual search of scientific literature in PubMed is laborious and time-consuming. Natural language processing (NLP) techniques along with artificial intelligence/machine learning approaches may allow for automatic processing in identifying DILI-related literature, but useful methods are yet to be demonstrated. To address this issue, we have developed an integrated NLP/machine learning classification model to identify DILI-related literature using only paper titles and abstracts. For prediction modeling, we used 14,203 publications provided by the Critical Assessment of Massive Data Analysis (CAMDA) challenge, employing word vectorization techniques in NLP in conjunction with machine learning methods. Classification modeling was performed using 2/3 of the data for training and the remainder for test in internal validation. The best performance was achieved using a linear support vector machine (SVM) model on the combined vectors derived from term frequency-inverse document frequency (TF-IDF) and Word2Vec, resulting in an accuracy of 95.0% and an F1-score of 95.0%. The final SVM model constructed from all 14,203 publications was tested on independent datasets, resulting in accuracies of 92.5%, 96.3%, and 98.3%, and F1-scores of 93.5%, 86.1%, and 75.6% for three test sets (T1-T3). Furthermore, the SVM model was tested on four external validation sets (V1-V4), resulting in accuracies of 92.0%, 96.2%, 98.3%, and 93.1%, and F1-scores of 92.4%, 82.9%, 75.0%, and 93.3%. Copyright © 2023 Oh, Tannenbaum and Deasy. |
| Keywords: | validation process; diagnostic accuracy; prediction; artificial intelligence; logistic regression analysis; drug-induced liver injury; machine learning; natural language processing; human; article; random forest; tf-idf; word2vec; linear support vector machine |
| Journal Title: | Frontiers in Genetics |
| Volume: | 14 |
| ISSN: | 1664-8021 |
| Publisher: | Frontiers Media S.A. |
| Date Published: | 2023-07-17 |
| Start Page: | 1161047 |
| Language: | English |
| DOI: | 10.3389/fgene.2023.1161047 |
| PROVIDER: | scopus |
| PMCID: | PMC10390074 |
| PUBMED: | 37529777 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Corresponding author is MSK author: Jung Hun Oh -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work