Extracting descriptions of evidence for drug-drug interactions from the biomedical literature
Date
2021
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Publisher
University of Delaware
Abstract
Drug-drug interactions (DDIs) can cause adverse drug effects, which result in morbidity and mortality. Predicting, anticipating, and avoiding them is thus an area of much concern. Studies of DDIs are typically published in the biomedical literature, however, gathering DDI information remains a challenging task given the rapidly increasing publication volume. In this thesis, we present a supervised learning approach to automatically collect evidence for DDIs from the biomedical literature. ☐ First, we identify the publications relevant to DDIs. The number of DDI relevant publications is relatively small compared to the vast amount of irrelevant publications. Models that attempt to identify publications relevant to DDIs without handling this imbalance are generally biased toward the irrelevant class, as the classifiers are trained on a dataset where most examples are irrelevant to DDIs. We propose a two-stage method to tackle this challenge. ☐ Potential DDIs can be verified with higher confidence if supporting evidence is provided. We thus focus on the descriptions of the evidence. Such evidence is typically described in statements conveying information about experimental methods and results. Analyzing such sentences can form the basis for effective retrieval of DDI knowledge. To identify the sentences containing evidential information, we introduce two methods based on statistical learning models and on neural networks. ☐ Different types of evidence for DDIs are reported in the biomedical literature. In vitro studies of drug interactions are conducted outside the living body, typically using animal or human cells, or material formed by recombination. In vivo studies focus on the impact of DDIs on living organisms. The drug interactions can also be discovered in clinical studies, which are used to check how well experimental treatments work as compared to the standard treatment for the same disease. Lack of any type of evidence is a knowledge gap that can hinder the process of transforming biomedical knowledge into clinical practice. Determining the type of evidence for DDIs forms a preliminary step toward systematically detecting and closing the gaps across the three types of evidence. We propose a supervised learning approach to identify the type of evidence for DDIs described in biomedical abstracts.
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Keywords
Biomedical text mining, Drug-drug interactions, Machine learning