[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127743-en":3,"doc-seo-127743-105":31,"detail-sidebar-cat-0-en-105":92},{"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},127743,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Machine learning versus regression for prediction of sporadic pancreatic cancer","BACKGROUND/OBJECTIVES: There is no widely accepted approach to identify patients at increased risk for sporadic pancreatic cancer (PC). This study compared two machine-learning models against a regression-based model for predicting pancreatic ductal adenocarcinoma (PDAC), the most common form of PC. METHODS: A retrospective cohort included patients aged 50–84 from Kaiser Permanente Southern California for training/internal validation (2008–2017) and the Veterans Affairs cohort for external testing. Performance of random survival forests (RSF) and eXtreme gradient boosting (XGB) was compared with COX proportional hazards regression (COX), including assessment of model heterogeneity. RESULTS and conclusions are reported.","UCLA  \nUCLA Previously Published Works  \nTitle  \nMachine learning versus regression for prediction of sporadic pancreatic cancer.  \nPermalink  \n[https://escholarship.org/uc/item/0jt2h9fx](https://escholarship.org/uc/item/0jt2h9fx)  \nJournal  \nPancreatology, 23(4)  \nAuthors  \nChen, Wansu  \nZhou, Botao Xie, Fagenet al.  \nPublication Date  \n2023-06-01  \nDOI  \n10.1016/j.pan.2023.04.009  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript  \n\n|  | HHS Public Access\u003Cbr>Author manuscript\u003Cbr>Pancreatology. Author manuscript; available in PMC 2024 June 01. |\n| --- | --- |\n\nPublished in final edited form as:  \nPancreatology. 2023 June ; 23(4): 396–402. doi:10.1016/j.pan.2023.04.009 .  \nMachine Learning versus Regression for Prediction of Sporadic Pancreatic Cancer  \nWansu Chen, PhD 1 , Botao Zhou, MS 1 , Christie Y. Jeon, ScD2 , Fagen Xie, PhD 1 , Yu-Chen Lin, MPH2 , Rebecca K. Butler, ScM 1 , Yichen Zhou, MS 1 , Tiffany Q. Luong, MPH 1 , Eva Lustigova, MPH 1 , Joseph R. Pisegna, MD3 , Bechien U. Wu, MD, MPH4  \n1Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA  \n2Cedars-Sinai Medical Center, Los Angeles, CA  \n3 Division of Gastroenterology and Hepatology, VA Greater Los Angeles Healthcare System, Los Angeles, CA and Departments of Medicine and Human Genetics David Geffen School of Medicine at UCLA  \n4Center for Pancreatic Care, Department of Gastroenterology, Los Angeles Medical Center, Southern California Permanente Medical Group, Los Angeles, CA  \nAbstract  \nBACKGROUND/OBJECTIVES: There is currently no widely accepted approach to identify patients at increased risk for sporadic pancreatic cancer (PC) . We aimed to compare the performance of two machine-learning models with a regression-based model in predicting pancreatic ductal adenocarcinoma (PDAC), the most common form of PC.  \nMETHODS: This retrospective cohort study consisted of patients 50–84 years of age enrolled in either Kaiser Permanente Southern California (KPSC, model training, internal validation) or the Veterans Affairs (VA, external testing) between 2008–2017. The performance of random survival forests (RSF) and eXtreme gradient boosting (XGB) models were compared to that of COX proportional hazards regression (COX). Heterogeneity of the three models were assessed.  \nCorrespondence: Wansu Chen, Ph.D., Department of Research and Evaluation, Kaiser Permanente Southern California, 100 S Los Robles, 2nd Floor, Pasadena, CA 91101, [Wansu.Chen@KP.org](Wansu.Chen@KP.org).  \nAuthor contributions: Wansu Chen: Conceptualization, Methodology, Software, Validation, Investigation, Resources, WritingOriginal Draft, Writing-Review & Editing, Visualization, Supervision; Botao Zhou: Methodology, Software, Validation, Formal analysis, Investigation, Writing-Review & Editing, Visualization; Christie Jeon: Conceptualization, Validation, Investigation, Writing-Review & Editing, Supervision; Fagen Xie: Methodology, Software, Formal analysis, Investigation, Data Curation, WritingReview & Editing; Yu-Chen Lin: Software, Validation, Investigation, Data Curation, Writing-Review & Editing; Rebecca Butler:  \nMethodology, Software, Formal analysis, Investigation, Data Curation, Writing-Review & Editing; Yichen Zhou: Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing-Review & Editing, Visualization; Tiffany Luong:  \nWriting-Review & Editing, Project administration; Eva Lustigova: Writing-Review & Editing, Supervision, Project administration; Joseph Pisegna: Writing-Review & Editing; Bechien Wu: Conceptualization, Methodology, Validation, Resources, Writing-Review & Editing, Supervision, Funding acquisition. All authors have approved the final submitted draft.  \nDisclosure Statement: The authors declare they have no conflict of interest for this study.  \nEthics approval and consent to participa","cbCaik7oodfCSaJC","https://ap.wps.com/l/cbCaik7oodfCSaJC","pdf",1596835,4,1,19,"English","en",105,"# Abstract\n## Background/Objectives\n## Methods\n## Results\n## Conclusions\n## Keywords","[{\"question\":\"What was the main goal of the study?\",\"answer\":\"To compare two machine-learning models (RSF and XGB) with a regression-based COX model for predicting PDAC risk in patients with sporadic pancreatic cancer.\"},{\"question\":\"What data sources were used for training and testing?\",\"answer\":\"Patients aged 50–84 from Kaiser Permanente Southern California were used for model training and internal validation, while the Veterans Affairs cohort was used for external testing.\"},{\"question\":\"Which models were evaluated in the comparison?\",\"answer\":\"Random survival forests (RSF), eXtreme gradient boosting (XGB), and COX proportional hazards regression (COX) were compared for predictive performance.\"}]","Machine learning versus regression for prediction of sporadic pancreatic cancer | 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