Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Tiwari, Vibha; Gupta, Rohit; Telang, Akshada; Tiwari, Akshra; Geddam, Rebakah; Awais, Muhammad; Khan, Muhammad Ahmed; Ghayvat, Hemant Title A name given to the resource AI-enhanced approaches for personalized cardiac treatment: insights from ECG data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived npj Systems Biology and Applications;Volume;12;Issue;1;Article No.;72; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1038/s41540-026-00702-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41540-026-00702-6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039872408?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039872408?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Tiwari V., Centre for Artificial Intelligence, Madhav Institute of Technology and Science (Deemed University), Madhya Pradesh, Gwalior, India, Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Vj Sweden; Gupta R., Centre for Artificial Intelligence, Madhav Institute of Technology and Science (Deemed University), Madhya Pradesh, Gwalior, India; Telang A., Centre for Artificial Intelligence, Madhav Institute of Technology and Science (Deemed University), Madhya Pradesh, Gwalior, India; Tiwari A., School of Sciences, Christ University, Uttar Pradesh, Ghaziabad, India; Geddam R., Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Vj Sweden, Unitedworld Institute of Technology, Karnavati University, Gujarat, Gandhinagar, India; Awais M., Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States; Khan M.A., Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Ghayvat H., Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Vj Sweden, IMT, Department of Humanities and Technology, Roskilde University, Universitetsvej 1, Roskilde, Denmark Description An account of the resource The analysis of drug-induced alterations in the electrocardiogram (ECG) is essential in measuring cardiac safety, but manual analysis is not always accurate enough to identify subtle but important effects. This paper examines how machine learning (ML) models can be used to categorize various pharmacological treatments according to their distinct ECG patterns to establish a platform of individualized therapeutic evaluation. Using the public ECG Effects of Dofetilide, Moxifloxacin, Dofetilide+Mexiletine, Dofetilide+Lidocaine and Moxifloxacin+Diltiazem (ECGDMMLD) database, key electrophysiological features were extractedincluding heart rate variability (HRV) and standard cardiac intervals (RR, PR, QT, QRS) to train and compare three different classifiers: XGBoost, Random Forest, and a Support Vector Machine (SVM). The analysis showed that tree-based ensemble techniques were very useful in this task. The XGBoost model had a better classification accuracy of 98.1%, which was closely followed by the random forest at 97.3%. Conversely, the SVM had much lower accuracy, implying that it was not as well adapted to the complexity of the high-dimensional ECG data. These results establish that ML models, particularly XGBoost, can accurately decode complex drug-induced cardiac signatures from ECG data. This work is a powerful demonstration of the proof-of-concept of automated and data-driven analytics integration into clinical processes to enhance drug safety and promote personalized medicine. The Author(s) 2026. Publisher An entity responsible for making the resource available Nature Research Relation A related resource ISSN: 20567189; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online