[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119061-en":3,"doc-seo-119061-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119061,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Formulation of a Computational Model for Predicting Drug Reactions Using Machine Learning","Efficient detection of drug reactions is critical for patient safety and for optimizing clinical treatment outcomes. This article formulates a computational model that predicts drug reactions in clinical settings using machine learning. The approach extracts signal from health records and prescription data, then analyzes associations between prescribed medications and observed patient reactions to flag potential adverse drug reactions earlier than manual methods. The study also targets needs such as scalability, real-time monitoring, and data integration.","Formulation of a Computational Model for Predicting Drug Reactions Using Machine Learning  \nChristopher Agbonkhesea*, Hettie Abimbola Soriyanb, Kolawole Mosac  \naLecturer, Department of Digital and Computational Studies, Bates College, Lewiston, ME 04240, USA bLecturer, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria:  \ncLecturer, School of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria  \n[a](aEmail:agbonschris@gmail.com)[Email:agbonschris@gmail.com](aEmail:agbonschris@gmail.com)  \n[b](bEmail:hasoriyan@gmail.com)[Email:hasoriyan@gmail.com](bEmail:hasoriyan@gmail.com)  \n[c](cEmail:kmosaku@oauife.edu.ng)[Email:kmosaku@oauife.edu.ng](cEmail:kmosaku@oauife.edu.ng)  \nAbstract  \nIn the rapidly evolving landscape of healthcare, the efficient detection of drug reactions is of paramount importance to ensure patient safety and optimize treatment outcomes. This article presents the formulation of a computational model for the prediction of drug reactions in clinical settings using machine learning techniques. Our research leverages state-of-the-art machine learning algorithms to extract valuable insights from health records and prescription data. By systematically analyzing the relationships between prescribed medications and observed patient reactions, our computational model will be able to identify potential drug reactions emanating from drug prescription in clinical a clinical setting.  \nKeywords: Artificial Intelligence; Machine Learning; Drug Reactions; Healthcare.  \n1. Introduction  \nIn recent years, Artificial Intelligence (AI) has gained adoption across several fields of life; from healthcare to agriculture, finance, science and technology, Law, manufacturing, education, transportation, and so on [1] . Specifically, the field of healthcare has undergone a rapid and transformative evolution, marked by groundbreaking advancements in medical technology, pharmaceuticals, and patient care.Among the myriad challenges facing modern healthcare, the detection of drug reactions has emerged as a critical concern both in orthodox medicine and traditional medicine [2] . Ensuring patient safety and optimizing treatment outcomes are paramount goals in the medical profession, and the early identification of adverse reactions to medications plays a pivotal role in achieving these objectives [3] .  \nReceived: 10/12/2023  \nAccepted: 11/16/2023  \nPublished: 11/26/2023  \n* Corresponding author.  \nHistorically, the process of identifying and managing drug reactions has relied heavily on manual surveillance, clinician reporting, and post-market surveillance systems [4] . While these methods have provided valuable insights into drug safety, they are often labor-intensive, time-consuming, and reactive in nature. This reactive approach may result in delayed detection of adverse reactions, potentially endangering patient health and increasing healthcare costs. Recognizing the limitations of traditional methods, the intersection of healthcare and artificial intelligence (AI) has opened up new possibilities for enhancing drug reaction detection [5] . Machine learning, a subset of AI, has emerged as a powerful tool for the early identification and proactive management of drug reactions. Leveraging the vast amounts of clinical data and prescription records available, machine learning algorithms can sift through this information to uncover hidden patterns and associations that might otherwise go unnoticed by human observers [6] . This manuscript presents a comprehensive study that underscores the transformative potential of machine learning in healthcare, particularly in the realm of drug reaction detection. By harnessing state-of-the-art machine learning techniques and advanced data analysis, this research aims to provide a proactive, data-driven solution to the age-old problem of identifying and managing drug reactions.This paper is a product of a broader research which aims at building a computatio","cbCaippSfJYrvfdQ","https://ap.wps.com/l/cbCaippSfJYrvfdQ","pdf",525461,1,10,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"Why is early detection of drug reactions important in healthcare?\",\"answer\":\"Early identification of adverse reactions supports patient safety and improves treatment outcomes. It also reduces delays that can increase risk and healthcare costs.\"},{\"question\":\"What is the main goal of the proposed study?\",\"answer\":\"The study formulates a computational model that uses machine learning to systematically predict potential drug reactions from prescription and clinical records.\"},{\"question\":\"How does the paper contrast the new approach with traditional drug-reaction monitoring?\",\"answer\":\"Traditional surveillance relies on manual clinician reporting and post-market systems, which are labor-intensive and reactive. The proposed model aims to provide proactive, data-driven detection.\"}]","Formulation of a Computational Model for Predicting Drug Reactions Using Machine Learning | PDF",1785722143,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"formulation-of-a-computational-model-for-predicting-drug-reactions-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/formulation-of-a-computational-model-for-predicting-drug-reactions-using-machine-learning/119061/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early detection of drug reactions important in healthcare?","Question",{"text":75,"@type":76},"Early identification of adverse reactions supports patient safety and improves treatment outcomes. It also reduces delays that can increase risk and healthcare costs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main goal of the proposed study?",{"text":80,"@type":76},"The study formulates a computational model that uses machine learning to systematically predict potential drug reactions from prescription and clinical records.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper contrast the new approach with traditional drug-reaction monitoring?",{"text":84,"@type":76},"Traditional surveillance relies on manual clinician reporting and post-market systems, which are labor-intensive and reactive. The proposed model aims to provide proactive, data-driven detection.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]