[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123177-en":3,"doc-seo-123177-105":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},123177,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Advances in the Clinical Application of Machine Learning in Acute Pancreatitis - a Review","Traditional disease prediction models and scoring systems for acute pancreatitis often fall short in delivering concise, reliable, and actionable predictions for progression and prognosis. As an interdisciplinary branch of artificial intelligence, machine learning is increasingly used to support severity assessment, complication detection, recurrence estimation, organ dysfunction prediction, and timing of surgical or interventional decisions. This review summarizes recent advances in ML-based models within acute pancreatitis and highlights their potential to strengthen future evidence-based clinical care.","TYPE Review  \nPUBLISHED 07 January 2025 DOI 10.3389/fmed.2024.1487271  \nOPEN ACCESS  \nEDITED BY  \nRahul Kashyap,  \nWellSpan Health, United States  \nREVIEWED BY  \nPriyal Mehta,  \nSaint Vincent Hospital, United States Muhammad Daniyal Waheed,  \nMaroof International Hospital, Pakistan  \n*CORRESPONDENCE  \nSenjun Jin  \n [jinsj_2008@163.com](jinsj_2008@163.com)  \nRECEIVED 27 August 2024  \nACCEPTED 16 December 2024  \nPUBLISHED 07 January 2025  \nCITATION  \nTan Z, Li G, Zheng Y, Li Q, Cai W, Tu J and Jin S (2025) Advances in the clinical application of machine learning in acute pancreatitis: a review.  \nFront. Med. 11:1487271 .  \ndoi: 10.3389/fmed.2024.1487271  \nCOPYRIGHT  \n© 2025 Tan, Li, Zheng, Li, Cai, Tu and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nAdvances in the clinical application of machine learning in acute pancreatitis: a review  \nZhaowang Tan  , Gaoxiang Li, Yueliang Zheng, Qian Li, Wenwei Cai, Jianfeng Tu and Senjun Jin*  \nEmergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China  \nTraditional disease prediction models and scoring systems for acute pancreatitis (AP) are often inadequate in providing concise, reliable, and effective predictions regarding disease progression and prognosis. As a novel interdisciplinary field within artificial intelligence (AI), machine learning (ML) is increasingly being applied to various aspects of AP, including severity assessment, complications, recurrence rates, organ dysfunction, and the timing of surgical intervention. This review focuses on recent advancements in the application of ML models in the context of AP.  \nKEYWORDS  \nartificial intelligence, machine-learning model, acute pancreatitis, severity, complications, recurrence, mortality  \n1 Introduction  \nAcute pancreatitis (AP) is an inflammatory disorder affecting the parenchyma and peripancreatic tissue, characterized by severe abdominal pain, elevated pancreatic enzymes, and pancreatitis-related changes on abdominal imaging. The incidence of AP has shown arising trend globally, with an average occurrence rate of 34 cases per 100,000 individuals. Approximately 20% of patients progress to either moderately severe acute pancreatitis (MSAP, accompanied by transient [􀀔48 h] organ dysfunction and/or local complications such as necrosis of pancreatic or peripancreatic tissue) or severe acute pancreatitis (SAP, accompanied by persistent [>48 h] organ failure), the mortality rate can reach as high as 20–40%( 1) .  \nMachine learning (ML) is a category of artificial intelligence tools in which virtual agents learn an optimized set of rules through trial and error—a policy that maximizes expected returns (2) . ML has many ideal characteristics that can help with medical decisionmaking, and these algorithms are able to infer the best decision from suboptimal training sets. ML has been successfully applied to medical problems in the past, such as diabetes and sepsis (3, 4) .  \nMachine learning has demonstrated significant potential in the field of medicine, particularly in disease diagnosis and prognosis. Over the past decade, the utilization of ML algorithms based on databases for acute pancreatitis has become increasingly prevalent.  \nFrontiers in Medicine 01 [frontiersin.org](frontiersin.org)  \nNumerous studies have employed ML algorithms to forecast AP mortality rates (5), severity (6–8), complications (9), recurrence rates ( 10), as well as surgical or intervention strategies (7), with ML exhibiting robust reliabil","cbCaibiA6q8vRF88","https://ap.wps.com/l/cbCaibiA6q8vRF88","pdf",489959,1,7,"English","en",105,"# Introduction\n## The role of ML in predicting AP mortality\n## The role of ML in severity assessment\n## The role of ML in predicting complications and recurrence\n## The role of ML in predicting organ dysfunction and timing of intervention","[{\"question\":\"为什么传统急性胰腺炎预测模型通常不够可靠？\",\"answer\":\"传统评分系统往往复杂且难以提供简洁、可靠且有效的进展与预后预测，预测效果存在局限。\"},{\"question\":\"机器学习在急性胰腺炎中主要用于哪些临床任务？\",\"answer\":\"机器学习被用于严重程度评估、并发症预测、复发率估计、器官功能障碍预测以及手术/干预时机判断等方面。\"},{\"question\":\"该综述重点关注机器学习在急性胰腺炎的哪些方面？\",\"answer\":\"综述聚焦急性胰腺炎场景下机器学习模型的最新应用进展，旨在梳理其在诊断与预后支持中的具体作用。\"}]","Advances in the Clinical Application of Machine Learning in Acute Pancreatitis - a Review | PDF",1785815040,18,{"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},"advances-in-the-clinical-application-of-machine-learning-in-acute-pancreatitis-a-review","",{"@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/advances-in-the-clinical-application-of-machine-learning-in-acute-pancreatitis-a-review/123177/",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","2026-08-04",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},"为什么传统急性胰腺炎预测模型通常不够可靠？","Question",{"text":75,"@type":76},"传统评分系统往往复杂且难以提供简洁、可靠且有效的进展与预后预测，预测效果存在局限。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"机器学习在急性胰腺炎中主要用于哪些临床任务？",{"text":80,"@type":76},"机器学习被用于严重程度评估、并发症预测、复发率估计、器官功能障碍预测以及手术/干预时机判断等方面。",{"name":82,"@type":73,"acceptedAnswer":83},"该综述重点关注机器学习在急性胰腺炎的哪些方面？",{"text":84,"@type":76},"综述聚焦急性胰腺炎场景下机器学习模型的最新应用进展，旨在梳理其在诊断与预后支持中的具体作用。","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]