[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127780-en":3,"doc-seo-127780-105":31,"detail-sidebar-cat-0-en-105":96},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127780,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","A Novel Prediction Model of the Risk of Pancreatic Cancer Among Diabetes Patients Using Multiple Clinical Data and Machine Learning","Pancreatic cancer has poor prognosis, and diabetes is a high-risk subgroup for developing pancreatic cancer. A retrospective observational study developed and evaluated a novel prediction model for pancreatic cancer risk among patients with type 2 diabetes using a multisite Taiwanese EMR database from 2009–2019. Machine learning algorithms, including logistic regression, LDA, gradient boosting, and random forest, were trained with predictors selected from literature and clinical perspectives. The best LDA model showed AUROC 0.9073, with key predictors including glucose, glycated hemoglobin, and medication-related factors.","The Texas Medical Center Library  \nDigitalCommons@TMC  \n\n| Faculty and Staff Publications | Baylor College of Medicine |\n| --- | --- |\n| 10-1-2023\u003Cbr>A Novel Prediction Model of the Risk of Pancreatic Cancer Among Diabetes Patients Using Multiple Clinical Data and Machine Learning\u003Cbr>Shih-Min Chen Phan Thanh Phuc Phung-Anh Nguyen Whitney Burton\u003Cbr>Shwu-Jiuan Lin\u003Cbr>Follow this and additional works at: [https://digitalcommons.library.tmc.edu/baylor_docs](https://digitalcommons.library.tmc.edu/baylor_docs)[ ](https://digitalcommons.library.tmc.edu/baylor_docs)See next page for additional authors\u003Cbr> Part of the Digestive System Diseases Commons, Endocrine System Diseases Commons, Endocrinology, Diabetes, and Metabolism Commons, Gastroenterology Commons, Medical Sciences Commons, Neoplasms Commons, and the Oncology Commons |  |\n\nRecommended Citation  \nChen, Shih-Min; Phuc, Phan Thanh; Nguyen, Phung-Anh; Burton, Whitney; Lin, Shwu-Jiuan; Lin, Weei-Chin; Lu, Christine Y; Hsu, Min-Huei; Cheng, Chi-Tsun; and Hsu, Jason C, \"A Novel Prediction Model of the Risk of Pancreatic Cancer Among Diabetes Patients Using Multiple Clinical Data and Machine Learning\"(2023) . Faculty and Staff Publications. 1597.  \n[https://digitalcommons.library.tmc.edu/baylor_docs/1597](https://digitalcommons.library.tmc.edu/baylor_docs/1597)  \nThis Article is brought to you for free and open access by the Baylor College of Medicine at  \nDigitalCommons@TMC. It has been accepted for inclusion in Faculty and Staff Publications by an authorized administrator of DigitalCommons@TMC. For more information, please contact [digcommons@library.tmc.edu](digcommons@library.tmc.edu).  \nAuthors  \nShih-Min Chen, Phan Thanh Phuc, Phung-Anh Nguyen, Whitney Burton, Shwu-Jiuan Lin, Weei-Chin Lin, Christine Y Lu, Min-Huei Hsu, Chi-Tsun Cheng, and Jason C Hsu  \nThis article is available at DigitalCommons@TMC: [https://digitalcommons.library.tmc.edu/baylor_docs/1597](https://digitalcommons.library.tmc.edu/baylor_docs/1597)  \nDOI: 10. 1002/cam4 .6547  \nRESEAR CH ARTICLE  \nA novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning  \nShih-Min Chen1 | Phan Thanh Phuc2  | Phung-Anh Nguyen3,4,5  | Whitney Burton2 | Shwu-Jiuan Lin1 | Weei-Chin Lin6 | Christine Y. Lu7,8,9 | Min-Huei Hsu3,10  | Chi-Tsun Cheng5 | Jason C. Hsu2,3,4,5   \n1School of Pharmacy, Taipei Medical University, Taipei, Taiwan  \n2International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan 3Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan  \n4Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan  \n5Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan  \n6Section of Hematology/Oncology, Department of Medicine and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA  \n7Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA 8Kolling Institute, Faculty of Medicine and Health, The University of Sydney and the Northern Sydney Local Health District, Sydney, New South Wales, Australia  \n9School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia 10Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan  \nCorrespondence  \nJason C. Hsu, International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan.  \n[Email:](Email: jasonhsu@tmu.edu.tw)[ jasonhsu@tmu.edu.tw](Email: jasonhsu@tmu.edu.tw)  \nFunding information  \nTaipei Medical University, Grant/ Award Number: TMU108-AE1-B42  \nAbstract  \nIntroduction: Pancreatic cancer is associated with poor prognosis. Consider","cbCaibo6yeVYW87a","https://ap.wps.com/l/cbCaibo6yeVYW87a","pdf",1063574,2,1,15,"English","en",105,"# Abstract\n## Introduction\n## Methods\n## Results\n## Conclusion","[{\"question\":\"What is the main purpose of the study?\",\"answer\":\"To develop a prediction model for pancreatic cancer risk among patients with diabetes using real-world clinical data and multiple machine-learning algorithms.\"},{\"question\":\"Which data and patient population were used to build the model?\",\"answer\":\"The study analyzed type 2 diabetes patients from a multisite Taiwanese EMR database collected between 2009 and 2019.\"},{\"question\":\"Which model performed best and what were its key performance metrics?\",\"answer\":\"The Linear Discriminant Analysis (LDA) model achieved the highest AUROC of 0.9073, with an accuracy of 84.03%, sensitivity of 0.8611, and specificity of 0.8403.\"},{\"question\":\"What predictors were identified as most significant for pancreatic cancer risk?\",\"answer\":\"The most significant predictors included glucose, glycated hemoglobin, hyperlipidemia comorbidity, and use of antidiabetic and lipid-modifying drugs.\"}]","A Novel Prediction Model of the Risk of Pancreatic Cancer Among Diabetes Patients Using Multiple Clinical Data and Machine Learning | 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