[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126715-en":3,"doc-seo-126715-105":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},126715,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","A Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug Development Portfolio Demand Planning","Accurate forecasting of clinical trial patient enrollment remains a major challenge because the stochastic enrollment process can drive development delays, extend trial duration and costs, and cause over- or under-estimation of clinical supply. This thesis proposes a machine learning model built on a Fully Convolutional Network (FCN). The model is trained on 100,000 enrollment records using patient demographics, disease and study details, sponsor and CRO information, and enrollment timing and geography to predict key enrollment characteristics. Testing on 5,000 records demonstrates high accuracy and supports improved portfolio demand planning.","University of Tennessee, Knoxville  \nTRACE: Tennessee Research and Creative Exchange  \n\n| Masters Theses | Graduate School |\n| --- | --- |\n| 5-2023\u003Cbr>A Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug Development Portfolio Demand Planning\u003Cbr>Ahmed Shoieb\u003Cbr>[ashoieb1@vols.utk.edu](ashoieb1@vols.utk.edu)\u003Cbr>Follow this and additional works at: [https://trace.tennessee.edu/utk_gradthes](https://trace.tennessee.edu/utk_gradthes)\u003Cbr> Part of the Artificial Intelligence and Robotics Commons, Clinical Epidemiology Commons, Clinical Trials Commons, Industrial Engineering Commons, Investigative Techniques Commons, Other Medical Sciences Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons, Pharmacy and Pharmaceutical Sciences Commons, and the Theory and Algorithms Commons |  |\n\nRecommended Citation  \nShoieb, Ahmed, \"A Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug Development Portfolio Demand Planning. \" Master's Thesis, University of Tennessee, 2023. [https://trace.tennessee.edu/utk_gradthes/9836](https://trace.tennessee.edu/utk_gradthes/9836)  \nThis Thesis is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Masters Theses by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [trace@utk.edu](trace@utk.edu).  \nTo the Graduate Council:  \nI am submitting herewith a thesis written by Ahmed Shoieb entitled \"A Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug Development Portfolio Demand Planning.\" I have examined the final electronic copy of this thesis for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Master of Science, with a major in Industrial Engineering.  \nAndrew Yu, Major Professor  \nWe have read this thesis and recommend its acceptance: John Kobza, James Simonton, Andrew Yu  \nAccepted for the Council: Dixie L. Thompson  \nVice Provost and Dean of the Graduate School  \n(Original signatures are on file with official student records.)  \nA Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug  \nDevelopment Portfolio Demand Planning  \nA Thesis Presented for the  \nMaster of Science  \nDegree  \nThe University of Tennessee, Knoxville  \nAhmed Shoieb  \nMay 2023  \nCopyright © 2023 by Ahmed Shoieb All rights reserved.  \nDEDICATION  \nI dedicate this work to my daughter, Ahd Ahmed Ahmed Shoieb. To my parents, Dr. Ahmed Mohamed Shoieb and Dr. Mona Elgayyar. To my wife, Yassmein Elgemaei.  \nI would not be the person Iam today without you all, I owe you everything.  \nACKNOWLEDGEMENTS  \nFirst and foremost, I would like to thank my thesis Committee Dr. Andrew Yu, Dr. John Kobza, and Dr. James Simonton for being world-class educators, their inspirational leadership, and their continuous support throughout this wonderful program.  \nI am profoundly thankful for my advisor, Dr. Andrew J. Yu, Department of Industrial and Systems Engineering at the Tickle College of Engineering for his valuable suggestions, continuous guidance, and encouragement in completing this research. I am especially grateful for his vast knowledge, empathy, and willingness to collaborate on such a rare research topic. Thankyou for everything, Dr. Yu. I would like to thank the faculty members in the Industrial and Systems Engineering Department for their support throughout the program, and profound knowledge.  \nI would also like to thank my family for their blessings, encouragement, and support throughout this program: My father Dr. Ahmed Mohamed Shoieb, my mother Dr. Mona Elgayyar, my sister Dr. Zienab Shoieb and her husband Ahmed Rizk, my brother Eng. Mohamed Shoieb, and my brother Yousef Shoieb.  \nFinally, I would like to thank my wife Yassmein Elgemaei for her patience and constant","cbCaio8AfHN64SPW","https://ap.wps.com/l/cbCaio8AfHN64SPW","pdf",1289168,1,69,"English","en",105,"# Table of Contents\n## Chapter 1 – Introduction\n## 1.1 Pharmaceutical Clinical Trial Supply Chain\n## 1.1.1 Challenges Faced by Clinical Supply Managers\n## 1.1.2 Technological Solutions for Challenges\n## 1.2 Pharmaceutical Clinical Trials and Patient Enrollment\n## 1.3 Research Objectives","[{\"question\":\"Why is predicting clinical trial patient enrollment difficult, and what impact does it have?\",\"answer\":\"Enrollment is stochastic, so inaccurate forecasts can delay drug development, increase clinical trial duration and costs, and lead to over- or under-estimation of clinical supply.\"},{\"question\":\"What machine learning approach does the thesis propose?\",\"answer\":\"The thesis proposes a machine learning model using a Fully Convolutional Network (FCN) trained on patient enrollment data.\"},{\"question\":\"What inputs are used to predict enrollment characteristics, and how is the model validated?\",\"answer\":\"Training uses 100,000 enrollment data points including age, gender, disease, investigational product, study phase, blinded status, sponsor/CRO selection, enrollment quarter, and enrollment country. The model is tested on 5,000 data points and achieves high accuracy.\"}]","A Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug Development Portfolio Demand Planning | PDF",1785934371,174,{"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},"a-machine-learning-approach-for-predicting-clinical-trial-patient-enrollment-in-drug-development-portfolio-demand-planning","",{"@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/a-machine-learning-approach-for-predicting-clinical-trial-patient-enrollment-in-drug-development-portfolio-demand-planning/126715/",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","2026-08-05",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 predicting clinical trial patient enrollment difficult, and what impact does it have?","Question",{"text":75,"@type":76},"Enrollment is stochastic, so inaccurate forecasts can delay drug development, increase clinical trial duration and costs, and lead to over- or under-estimation of clinical supply.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning approach does the thesis propose?",{"text":80,"@type":76},"The thesis proposes a machine learning model using a Fully Convolutional Network (FCN) trained on patient enrollment data.",{"name":82,"@type":73,"acceptedAnswer":83},"What inputs are used to predict enrollment characteristics, and how is the model validated?",{"text":84,"@type":76},"Training uses 100,000 enrollment data points including age, gender, disease, investigational product, study phase, blinded status, sponsor/CRO selection, enrollment quarter, and enrollment country. 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