[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120419-en":3,"doc-seo-120419-105":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},120419,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","An Approach to Rapidly Assess Sepsis Using Machine Learning Approach - Thesis","Sepsis is a life-threatening condition, and timely patient stratification depends on understanding disease pathophysiology through host immune response biomarkers. The work addresses limitations of accurate sepsis endotyping that hinder prompt clinical decisions and optimal sepsis management. It demonstrates a data-driven validation model using machine learning to predict sepsis host-immune response for stratification. Multiple biomarkers from a single plasma sample—IL-6, IL-8, IL-10, IP-10, TRAIL, PCT, and CRP—are used, with supervised methods achieving 96.64% and 94.64% accuracy and clustering reaching a silhouette score of 0.5.","AN APPROACH TO RAPIDLY ASSESSES SEPSIS USING MACHINE LEARNING  \nAPPROACH  \nby  \nAbha Umesh Sardesai  \nAPPROVED BY SUPERVISORY COMMITTEE:  \nDr. Shalini Prasad, Chair  \nDr. Dinesh Bhatia  \nDr. Sriram Muthukumar  \nCopyright 2021 Abha Umesh Sardesai  \nAll Rights Reserved  \nDedicated to my parents and family.  \nAN APPROACH TO RAPIDLY ASSESS SEPSIS USING MACHINE LEARNING  \nAPPROACH  \nby  \nABHA UMESH SARDESAI, BEng  \nTHESIS  \nPresented to the Faculty of  \nThe University of Texas at Dallas  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nMASTER OF SCIENCE IN  \nCOMPUTER ENGINEERING  \nTHE UNIVERSITY OF TEXAS AT DALLAS  \nDecember 2021  \nACKNOWLEDGMENTS  \nThe journey of pursuing my MS degree has been one of the most holistic, encouraging, and extraordinary experiences of my life. It has been full of opportunities to learn, understand, implement, and improve upon unique designs as well as my skills. I would like to take this opportunity to thank the people who have made this journey a success and joyful experience.  \nI extend my sincere thanks to my adviser, Dr. Shalini Prasad for her accurate guidance, immense support, and privilege to choose my research. She is a great source of inspiration, advice, and company. She has always been open to my innovative ideas for which I am thankful. I also thank Dr. Dinesh Bhatia forbeing a great source of advice and foresightedness. I acknowledge Dr. Sriram Muthukumar for valuable discussions in the spirit of my quest of knowledge.  \nThe BMNL lab team has been a very supportive structure that I needed to learn the importance of teamwork. I thank my colleagues, especially Miss Ambalika Tanak for her guidance and support.  \nI would also like to thank the people who invested their time to make me the person I am today. I thank the supreme power that drives the world, the Almighty. I thank my mother Anagha, father Umesh for their love and support. My brother Archis has been the source of joy in my life. I will always remain indebted to my family.  \nJune 2021  \nAN APPROACH TO RAPIDLY ASSESS SEPSIS USING MACHINE LEARNING  \nAPPROACH  \nAbha Umesh Sardesai, MS  \nThe University of Texas at Dallas, 2021  \nSupervising Professor: Dr. Shalini Prasad  \nSepsis is a life-threatening condition and understanding the disease pathophysiology using host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians to make timely decisions alongside insufficiencies in appropriate sepsis management. The objective of this work is to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decision to predict sepsis host-immune response. Herein, we used machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurement from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10, TRAIL, PCT and CRP where the test dataset was 70% . Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed promising accuracies of 96.64% and 94.64% respectively. Using unsupervised clustering algorithms, we are able to achieve silhouette score of positive 0.5. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.  \nTABLE OF CONTENTS  \nACKNOWLEDGMENTS ...................................................... v  \n[ABSTRACT .......................................................](ABSTRACT ................................................................ vi)[.........](ABSTRACT ................................................................ vi)[ vi](ABSTRACT ................................................................ vi)  \n[LIST OF FIGURES .................................................](LIST OF FIGURES .......................................................","cbCaintsfTRTcpSQ","https://ap.wps.com/l/cbCaintsfTRTcpSQ","pdf",863746,1,46,"English","en",105,"# Acknowledgments\n# Abstract\n# List of Figures\n## Figure 1\n## Figure 2\n# Table of Contents\n## Chapter 1 Introduction\n## Chapter 2 Materials and Methods\n## Chapter 3 Results and Discussions\n## Chapter 4 Conclusion\n# References\n# Biographical Sketch\n# Curriculum Vitae","[{\"question\":\"What is the main objective of the proposed work on sepsis?\",\"answer\":\"To demonstrate the feasibility of a data-driven validation model that supports clinical decision-making by predicting sepsis host-immune response for patient stratification.\"},{\"question\":\"Which biomarkers are used to build the machine learning model?\",\"answer\":\"The model combines multiple biomarker measurements from a single plasma sample, including IL-6, IL-8, IL-10, IP-10, TRAIL, PCT, and CRP.\"},{\"question\":\"How do the supervised and unsupervised methods perform in the results?\",\"answer\":\"Supervised naïve Bayes and decision tree algorithms achieve accuracies of 96.64% and 94.64%, while unsupervised clustering yields a silhouette score of 0.5.\"}]","An Approach to Rapidly Assess Sepsis Using Machine Learning Approach - Thesis | PDF",1785729948,116,{"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},"an-approach-to-rapidly-assess-sepsis-using-machine-learning-approach-thesis","",{"@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/an-approach-to-rapidly-assess-sepsis-using-machine-learning-approach-thesis/120419/",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},"What is the main objective of the proposed work on sepsis?","Question",{"text":75,"@type":76},"To demonstrate the feasibility of a data-driven validation model that supports clinical decision-making by predicting sepsis host-immune response for patient stratification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which biomarkers are used to build the machine learning model?",{"text":80,"@type":76},"The model combines multiple biomarker measurements from a single plasma sample, including IL-6, IL-8, IL-10, IP-10, TRAIL, PCT, and CRP.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the supervised and unsupervised methods perform in the results?",{"text":84,"@type":76},"Supervised naïve Bayes and decision tree algorithms achieve accuracies of 96.64% and 94.64%, while unsupervised clustering yields a silhouette score of 0.5.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]