[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118150-en":3,"doc-seo-118150-105":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},118150,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Empowering advanced medical decision-making through machine learning in healthcare - Kumulative Dissertation","This cumulative dissertation investigates how machine learning can improve advanced medical decision-making in healthcare through three distinct, publication-based contributions. It analyzes applications and methodologies that support better operational planning, enhanced sensitivity in binary classification using customized activation functions, and analytics-driven COVID-19 triage by transforming a human-made algorithm for predicting clinical pathways. The work evaluates performance, practical relevance, and algorithmic explainability across the presented contribution areas.","Empowering advanced medical decisionmaking through machine learning in  \nhealthcare  \nKumulative Dissertation  \nder Wirtschaftswissenschaftlichen Fakultät der Universität Augsburg zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften  \n(Dr. rer. pol.)  \nVorgelegt von Milena Grieger, M.Sc.  \nErstgutachter: Prof. Dr. Jens O. Brunner  \nZweitgutachter: Prof. Dr. Sebastian Schiffels  \nVorsitzender der mündlichen Prüfung: Prof. Dr. Axel Tuma  \nList of Contributions  \nThis thesis contains the following contributions submitted to or published in scientific journals. The specified categories relate to the journal ranking VHBJOURQUAL3 of the Verband der Hochschullehrer für Betriebswirtschaft e.V.(2015) . The order of the contributions corresponds to the order of print in this thesis.  \nContribution 1: Grieger, M, Brunner, JO, Heller, AR, Bartenschlager, CC (2023) . Scarce, scarcer, scarcest: Performance-flexible AI-based planning of elective surgeries for efficient and effective intensive care capacity management.  \nStatus: Submitted on August 31, 2023, to OR Spectrum; Category A.  \nContribution 2: Grieger, M, Shala, E, Schüller, M, Ebel, SS, Brunner, JO, Vehreshild, JJ, Erber, J, Hanses, F, Zabel, LT, Römmele, C, Shmygalev, S, Bartenschlager, CC (2023) . DENLU and leaky stanh: customized activation functions targeting enhanced sensitivity with healthcare applications in binary classification.  \nStatus: Close to submission.  \nContribution 3: Bartenschlager, CC, Grieger, M, Erber, J, Neidel, T, Borgmann, S, Vehreschild, JJ, Steinbrecher, M, Rieg, S, Stecher, M, Dhillon, C, Ruethrich, MM, Jakob, CEM, Hower, M, Heller, AR, Vehreschild, M, Wyen, C, Messmann, H, Pipel, C, Brunner, JO, Hanses, F, Römmele, C (2023) . Covid- 19 triage in the emergency department 2.0: How analytics and AI transform a human-made algorithm for the prediction of clinical pathways. Status: Published in Health Care Management Science; Category A.  \nAcknowledgments  \nI would like to express my deepest appreciation to my academic advisor, Prof. Dr. Jens O. Brunner, for your unwavering support, unshakeable confidence, and invaluable academic insights that have enriched my journey over the past three years. The countless discussions we've engaged in have been not only enlightening but also instrumental in shaping my academic growth. I also want to express my deep gratitude to Prof. Dr. Christina Bartenschlager for her profound and scientific support, which has greatly contributed to the depth and quality of my work. Additionally, I want to acknowledge and thank the remarkable individuals I had the privilege of working with. Your collaboration has made the academic environment not only intellectually stimulating but also an enjoyable place to spend time, both within and beyond the confines of our offices.  \nI could not have undertaken this journey without my family and friends. Your unwavering belief in me has consistently fueled my motivation and offered me with invaluable support. I want to convey my deep gratitude to you, Robin, particularly for being a constant source of both support and balance, even during the most stressful times, and for the joy you've brought into my life.  \nPerhaps we should all stop for a moment and focus not only on making our AI better and more successful but also on the benefit of humanity.  \nSTEPHEN HAWKING  \nContents  \n1 INTRODUCTION .................................................................. 1  \n1.1 MOTIVATION.............................................................................................. 1  \n1.2 INTRODUCTION TO MACHINE LEARNING ...................................................... 2  \n1.3 MACHINE LEARNING IN HEALTHCARE .........................................................4  \n1.4 ORGANIZATION OF THIS THESIS .................................................................. 5  \n2 SUMMARY OF THE CONTRIBUTIONS ................................. 7  \n2.1 SCARCE, SCARCER, SCARCEST: PERFORMANC","cbCailV3BHaaVibo","https://ap.wps.com/l/cbCailV3BHaaVibo","pdf",2315959,1,103,"English","en",105,"# 1 Introduction\n## 1.1 Motivation\n## 1.2 Introduction to Machine Learning\n## 1.3 Machine Learning in Healthcare\n## 1.4 Organization of This Thesis\n# 2 Summary of the Contributions\n## 2.1 Scarce, Scarcest: Performance-flexible AI-based planning of elective surgeries for intensive care capacity management\n## 2.2 DENLU and leaky stanh: customized activation functions for sensitivity-enhanced binary classification in healthcare\n## 2.3 Covid-19 triage in the emergency department 2.0: analytics and AI for predicting clinical pathways\n# 3 Discussion of the Contributions\n## 3.1 Can a loss function be harnessed to empower decision-makers in flexible ICU capacity planning?\n## 3.2 Can customized AFs enhance sensitivity-based binary classification performance?\n## 3.3 Can integrating analytics and machine learning improve COVID-19 triage for clinical pathways while ensuring explainability?\n# 4 Conclusion\n# References\n# Appendix A: Performance-flexible AI\n# Appendix B: Customized AFs\n# Appendix C: COVID-19 Triage","[{\"question\":\"What is the core research focus of this thesis?\",\"answer\":\"The thesis examines how machine learning applications and methodologies can positively influence advanced medical decision-making in healthcare through three distinct contributions.\"},{\"question\":\"What contribution is related to intensive care capacity management?\",\"answer\":\"One contribution proposes performance-flexible AI-based planning of elective surgeries to manage intensive care capacity efficiently and effectively.\"},{\"question\":\"How does the thesis address COVID-19 triage?\",\"answer\":\"It presents an approach called “Emergency Department 2.0” that uses analytics and AI to transform a human-made algorithm for predicting clinical pathways, while also discussing explainability considerations.\"}]","Empowering advanced medical decision-making through machine learning in healthcare - 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