Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

Salman, Muhammad and Munawar, Hafiz Suliman and Latif, Khalid and Akram, Muhammad Waseem and Khan, Sara Imran and Ullah, Fahim ORCID: https://orcid.org/0000-0002-6221-1175 (2022) Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare. Big data and cognitive computing, 6 (1):30. pp. 1-17.

[img]
Preview
Text (Published Version)
BDCC-06-00030.pdf
Available under License Creative Commons Attribution 4.0.

Download (2MB) | Preview

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.


Statistics for USQ ePrint 47366
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Surveying and Built Environment (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Surveying and Built Environment (1 Jan 2022 -)
Date Deposited: 24 Mar 2022 00:29
Last Modified: 24 Mar 2022 00:29
Uncontrolled Keywords: drug–drug interaction; information extraction; natural language processing; deep learning; severity; smart healthcare; technologies
Fields of Research (2020): 42 HEALTH SCIENCES > 4203 Health services and systems > 420302 Digital health
42 HEALTH SCIENCES > 4203 Health services and systems > 420311 Health systems
46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460903 Information modelling, management and ontologies
32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320207 Emergency medicine
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning
Identification Number or DOI: https://doi.org/10.3390/bdcc6010030
URI: http://eprints.usq.edu.au/id/eprint/47366

Actions (login required)

View Item Archive Repository Staff Only