The Multiple Approaches for Drug-Drug Interaction Extraction using Machine learning and transformer based Model

The Multiple Approaches for Drug-Drug Interaction Extraction using Machine learning and transformer based Model | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 30 October 2025 V1 Latest version Share on The Multiple Approaches for Drug-Drug Interaction Extraction using Machine learning and transformer based Model Authors : Gurpreet Singh 0000-0003-3584-1283 [email protected] and Sundareswari Thiyagarajan Authors Info & Affiliations https://doi.org/10.22541/au.176184164.48760891/v1 163 views 156 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This research paper investigates a machine learning based approach for Drug-Drug Interaction (DDI) extraction for determining the side effects of multi drugs when prescribed simultaneously. In our proposed model we used TAC 2017 Dataset, which has Adverse Drug Reactions (ADRs) data for the classification ofdrug-drug interaction. TAC 2017 Dataset has various types of information which are related to drugs and their interactions. Our method uses Term Frequency-Inverse Document Frequency (TF-IDF) to transform the textual descriptions ofside effects for DDI into numerical feature vectors, followed by a Random Forest Classifier, Gradient Boosting, BioBERT, Support Vector Machine (SVM) Algorithms to predict the potential interactions between drug pairs. One of the key strength of the Random Forest approach is its ability to provide feature importance scores, which allows us to interpret which side effects are most influential in predicting drug interactions. The key advantage of Gradient Boosting is its high predictive performance combined with interpretability. It is able to handle complex, structured data efficiently. Additionally, the model's decisions are more transparent, which is necessary in the biomedical domain. The advantageof SVM is its ability to handle high-dimensional data, capture complex non-linear interactions using kernel functions, and generalize with datasets, making it robust to over-fitting. BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) is advantageous for DDI prediction due to its biomedical domain knowledge, contextual understanding of complex drug-related texts.These method captures the relevance and importance of each side effect of multi drugs and also generate pairs of drugs from the dataset. Our model demonstrates competitive performance in DDI prediction, which highlights the utility of text-based feature extraction combined with an interpretable ensemble learning model. Supplementary Material File (ssrn-5640432-2.pdf) Download 1.13 MB Information & Authors Information Version history V1 Version 1 30 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords adverse drug reactions aerospace biobert bioengineering communication, networking and broadcast technologies components, circuits, devices and systems drug-drug interaction engineered materials, dielectrics and plasmas engineering profession general topics for engineers gradient boosting algorithm random forest classifier side effects of support vector machine Authors Affiliations Gurpreet Singh 0000-0003-3584-1283 [email protected] View all articles by this author Sundareswari Thiyagarajan View all articles by this author Metrics & Citations Metrics Article Usage 163 views 156 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Gurpreet Singh, Sundareswari Thiyagarajan. The Multiple Approaches for Drug-Drug Interaction Extraction using Machine learning and transformer based Model. Authorea . 30 October 2025. DOI: https://doi.org/10.22541/au.176184164.48760891/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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