Abstract
The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients.
Highlights
In the last couple of decades, biomedical and smart healthcare research has witnessed a rapid amount of growth in terms of its presence in the literature, novel discoveries, and computational approaches, due to which a huge amount of experimental data has been generated and published (i.e., Big Data) to validate and to describe these innovations [1].The amount of data is so large that it is impossible for a human being to analyze and read the articles related to their desired field, and even the simple extraction of field-related published articles has become nearly impossible
Each of these three metrics are critically important in biomedical-related tasks and in drug–drug interactions (DDI) extraction, our model scored more than 80% in all three metrics, i.e., 83.81, 81.59, 82.68 in precision, recall and f-measure, respectively
Employment of long short-term memory (LSTM) from both ends of the sentence resolved the issue of bias which was critical in the DDI extraction task where most significant words could be anywhere in a sentence, i.e., before, after or around the subject
Summary
In the last couple of decades, biomedical and smart healthcare research has witnessed a rapid amount of growth in terms of its presence in the literature, novel discoveries, and computational approaches, due to which a huge amount of experimental data has been generated and published (i.e., Big Data) to validate and to describe these innovations [1].The amount of data is so large that it is impossible for a human being to analyze and read the articles related to their desired field, and even the simple extraction of field-related published articles has become nearly impossible. To utilize the information from scientific publications and articles, the increased use of automation due to the diversification and explosive growth in the healthcare literature as well as in the pharmaceutical industry is a clear representation of this growth. This is why attention has been diverted and focused on the implementation and evolution of tools for knowledge-based discovery: the tremendous amount of biomedical research in the literature requires automation to extract, represent, interpret, and maintain this information in a refined manner. The motive of these organizations is to ensure patient safety, which is considered to be the of the highest priority, and, to achieve this purpose, these organizations are actively participating in taking legislative measures to control the adverse effects that can be avoided through the use of proper strategies and plans
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