{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:04:07Z","timestamp":1775469847540,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The detection and classification of drug\u2013drug 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 \u2018Big Data\u2019 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\u2013Drug 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.<\/jats:p>","DOI":"10.3390\/bdcc6010030","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T01:53:54Z","timestamp":1646790834000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Big Data Management in Drug\u2013Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare"],"prefix":"10.3390","volume":"6","author":[{"given":"Muhammad","family":"Salman","sequence":"first","affiliation":[{"name":"Research School of Computing, The Australian National University (ANU), Canberra, ACT 2601, Australia"},{"name":"Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8492-0274","authenticated-orcid":false,"given":"Hafiz Suliman","family":"Munawar","sequence":"additional","affiliation":[{"name":"School of the Built Environment, University of New South Wales, Sydney, NSW 2052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khalid","family":"Latif","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Waseem","family":"Akram","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sara Imran","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Chemical Engineering, University of New South Wales, Kensington, Sydney, NSW 2052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6221-1175","authenticated-orcid":false,"given":"Fahim","family":"Ullah","sequence":"additional","affiliation":[{"name":"School of Surveying and Built Environment, University of Southern Queensland, Springfield Central, QLD 4300, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"ref_1","unstructured":"Kohn, L.T., Corrigan, J.M., and Donaldson, M.S. 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