{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:50:31Z","timestamp":1790704231914,"version":"4.1.0"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Pancreatic pseudocysts (PPC) are a late local complication of acute pancreatitis (AP). Persistent PPC carry a high risk of severe outcomes. Existing models, which are predominantly based on logistic regression, exhibit limited predictive performance and have not undergone temporal validation. This study aimed to develop and validate an interpretable machine learning model using routinely available clinical data for classifying AP patients according to PPC development status.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      We retrospectively analyzed 1,184 AP patients admitted to a tertiary hospital between 2018 and 2023. Data from 2018 to 2022 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009979) were randomly split into training (70%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009685) and internal test (30%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009294) sets, while the 2023 cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009205) served as an independent temporal validation set. Candidate predictors\u2014including demographic characteristics, clinical history, and routine laboratory parameters\u2014were screened via univariate analysis and further selected using LASSO regression to address multicollinearity. Nine machine learning algorithms were developed and compared: logistic regression, decision tree, random forest, artificial neural network, support vector machine, K-nearest neighbors, na\u00efve Bayes, XGBoost, and LightGBM. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. The optimal model was interpreted using SHapley Additive exPlanations (SHAP).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LASSO regression selected seven predictors: diabetes history, pancreatitis history, biliary surgery history, C-reactive protein, albumin, blood urea nitrogen, and serum calcium. The random forest model demonstrated the best classification performance, achieving an AUC of 0.884 (95% CI: 0.827\u20130.941) on the internal test set and 0.914 (95% CI: 0.845\u20130.983) on the temporal validation set. SHAP analysis identified serum calcium and C-reactive protein as the most important predictors, with low calcium and elevated CRP substantially increasing the probability of PPC classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>We developed and temporally validated interpretable machine learning models for classifying PPC development status using seven routinely available clinical indicators. The random forest model demonstrated excellent discrimination and generalizability, while SHAP analysis provided transparent explanations of individual classifications. These models may facilitate early identification of high-risk AP patients and guide proactive clinical management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1753529","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T14:12:37Z","timestamp":1773756757000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and interpretable machine learning models for classification of pancreatic pseudocyst risk in acute pancreatitis"],"prefix":"10.3389","volume":"8","author":[{"given":"Hailong","family":"Feng","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology","place":["Luoyang, Henan, China"]},{"name":"Henan Medical Key Laboratory of Gastrointestinal Microecology and Hepatology","place":["Luoyang, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology","place":["Luoyang, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihan","family":"He","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology","place":["Luoyang, Henan, China"]},{"name":"Henan Medical Key Laboratory of Gastrointestinal Microecology and Hepatology","place":["Luoyang, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liwei","family":"Shang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology","place":["Luoyang, Henan, China"]},{"name":"Henan Medical Key Laboratory of Gastrointestinal Microecology and Hepatology","place":["Luoyang, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingrui","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology","place":["Luoyang, Henan, China"]},{"name":"Henan Medical Key Laboratory of Gastrointestinal Microecology and Hepatology","place":["Luoyang, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keyang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology","place":["Luoyang, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kejia","family":"An","sequence":"additional","affiliation":[{"name":"College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology","place":["Luoyang, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology","place":["Luoyang, Henan, China"]},{"name":"Henan Medical Key Laboratory of Gastrointestinal Microecology and Hepatology","place":["Luoyang, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1053\/j.gastro.2021.09.043","article-title":"Global incidence of acute pancreatitis is increasing over time: a systematic review and meta-analysis","volume":"162","author":"Iannuzzi","year":"2022","journal-title":"Gastroenterology"},{"key":"B2","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/s2468-1253(16)30004-8","article-title":"Global incidence and mortality of pancreatic diseases: a systematic review, meta-analysis, and meta-regression of population-based cohort studies","volume":"1","author":"Xiao","year":"2016","journal-title":"Lancet Gastroenterol Hepatol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1016\/s0140-6736(20)31310-6","article-title":"Acute pancreatitis","volume":"396","author":"Boxhoorn","year":"2020","journal-title":"Lancet (London, England)"},{"key":"B4","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1136\/gutjnl-2012-302779","article-title":"Classification of acute pancreatitis\u20132012: revision of the Atlanta classification and definitions by international consensus","volume":"62","author":"Banks","year":"2013","journal-title":"Gut"},{"key":"B5","doi-asserted-by":"publisher","first-page":"e36102","DOI":"10.1097\/md.0000000000036102","article-title":"New model for predicting the development of pancreatic pseudocyst secondary to acute pancreatitis","volume":"102","author":"Hou","year":"2023","journal-title":"Medicine (Baltimore)"},{"key":"B6","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1016\/j.pan.2024.08.007","article-title":"A nomogram to predict the occurrence of pseudocyst in patients with acute pancreatitis","volume":"24","author":"Ji","year":"2024","journal-title":"Pancreatology"},{"key":"B7","doi-asserted-by":"publisher","first-page":"159","DOI":"10.3892\/etm.2020.9590","article-title":"Pancreatic pseudocyst: dilemma of its recent management (review)","volume":"21","author":"Tan","year":"2021","journal-title":"Exp Ther Med"},{"key":"B8","doi-asserted-by":"publisher","first-page":"e747","DOI":"10.1002\/iid3.747","article-title":"Prediction of acute pancreatitis complications using routine blood parameters during early admission","volume":"10","author":"Chen","year":"2022","journal-title":"Immun Inflamm Dis"},{"key":"B9","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A guide to machine learning for biologists","volume":"23","author":"Greener","year":"2022","journal-title":"Nat Rev Mol Cell Biol"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the future\u2014big data, machine learning, and clinical medicine","volume":"375","author":"Obermeyer","year":"2016","journal-title":"N Engl J Med"},{"key":"B11","doi-asserted-by":"publisher","first-page":"536","DOI":"10.4149\/bll_2019_087","article-title":"Overview of artificial neural network models in the biomedical domain","volume":"120","author":"Renganathan","year":"2019","journal-title":"Bratisl Lek Listy"},{"key":"B12","doi-asserted-by":"publisher","first-page":"2194436","DOI":"10.1080\/0886022x.2023.2194436","article-title":"Development and validation of a prediction model for the early occurrence of acute kidney injury in patients with acute pancreatitis","volume":"45","author":"Wu","year":"2023","journal-title":"Renal Fail"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1718","DOI":"10.3390\/jcm12051718","article-title":"To establish an early prediction model for acute respiratory distress syndrome in severe acute pancreatitis using machine learning algorithm","volume":"12","author":"Zhang","year":"2023","journal-title":"J Clin Med"},{"key":"B14","doi-asserted-by":"publisher","first-page":"103074","DOI":"10.1016\/j.eclinm.2025.103074","article-title":"Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosis","volume":"80","author":"Ning","year":"2025","journal-title":"EClinicalMedicine"},{"key":"B15","doi-asserted-by":"publisher","first-page":"20552076251346361","DOI":"10.1177\/20552076251346361","article-title":"Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms","volume":"11","author":"Song","year":"2025","journal-title":"Digital Health"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1638097","DOI":"10.3389\/fmed.2025.1638097","article-title":"Machine learning-based predictive model for acute pancreatitis-associated lung injury: a retrospective analysis","volume":"12","author":"Du","year":"2025","journal-title":"Front Med (Lausanne)"},{"key":"B17","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1007\/s00464-022-09427-z","article-title":"Analysis of pancreatic pseudocyst drainage procedural outcomes: a population based study","volume":"37","author":"Chawla","year":"2023","journal-title":"Surg Endosc"},{"key":"B18","doi-asserted-by":"publisher","first-page":"419","DOI":"10.14309\/ajg.0000000000002645","article-title":"American College of gastroenterology guidelines: management of acute pancreatitis","volume":"119","author":"Tenner","year":"2024","journal-title":"Am J Gastroenterol"},{"key":"B19","doi-asserted-by":"publisher","first-page":"104641","DOI":"10.1016\/j.ijmedinf.2021.104641","article-title":"Machine learning predictive models for acute pancreatitis: a systematic review","volume":"157","author":"Zhou","year":"2022","journal-title":"Int J Med Inf"},{"key":"B20","doi-asserted-by":"publisher","first-page":"605","DOI":"10.3748\/wjg.v28.i5.605","article-title":"Machine learning models and over-fitting considerations","volume":"28","author":"Charilaou","year":"2022","journal-title":"World J Gastroenterol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1016\/j.pan.2021.05.007","article-title":"Early occurrence of pseudocysts in acute pancreatitis\u2014a multicenter international cohort analysis of 2275 cases","volume":"21","author":"Szak\u00f3","year":"2021","journal-title":"Pancreatology"},{"key":"B22","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1111\/joim.12822","article-title":"Edoctor: machine learning and the future of medicine","volume":"284","author":"Handelman","year":"2018","journal-title":"J Intern Med"},{"key":"B23","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1016\/j.jval.2019.02.012","article-title":"Machine learning for health services researchers","volume":"22","author":"Doupe","year":"2019","journal-title":"Value Health"},{"key":"B24","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1038\/s41575-019-0158-2","article-title":"New insights into acute pancreatitis","volume":"16","author":"Lee","year":"2019","journal-title":"Nat Rev Gastroenterol Hepatol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.4103\/0028-3886.391388","article-title":"Study on the correlation between blood urea nitrogen, creatinine level, proteinuria and Parkinson\u2019s disease","volume":"71","author":"Yang","year":"2023","journal-title":"Neurol India"},{"key":"B26","doi-asserted-by":"publisher","first-page":"4241","DOI":"10.11152\/mu-4241","article-title":"Left renal vein thrombosis due to pancreatic pseudocyst compression","volume":"25","author":"Jiang","year":"2023","journal-title":"Med Ultrason"},{"key":"B27","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1097\/MPA.0b013e3182374def","article-title":"Acute pancreatic pseudocyst: incidence, risk factors, and clinical outcomes","volume":"41","author":"Kim","year":"2012","journal-title":"Pancreas"},{"key":"B28","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1186\/s12876-018-0874-z","article-title":"Identification of risk factors for pancreatic pseudocysts formation, intervention and recurrence: a 15-year retrospective analysis in a tertiary hospital in China","volume":"18","author":"Tan","year":"2018","journal-title":"BMC Gastroenterol"},{"key":"B29","doi-asserted-by":"publisher","first-page":"5297143","DOI":"10.1155\/2017\/5297143","article-title":"Serum albumin is independently associated with persistent organ failure in acute pancreatitis","volume":"2017","author":"Hong","year":"2017","journal-title":"Can J Gastroenterol Hepatol"},{"key":"B30","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s12876-017-0615-8","article-title":"Serum albumin, a good indicator of persistent organ failure in acute pancreatitis","volume":"17","author":"Li","year":"2017","journal-title":"BMC Gastroenterol"},{"key":"B31","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.sjbs.2018.11.012","article-title":"Correlation between the glucose level and the development of acute pancreatitis","volume":"26","author":"Sun","year":"2019","journal-title":"Saudi J Biol Sci"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.2337\/dc11-1925","article-title":"Effect of diabetes on severity and hospital mortality in patients with acute pancreatitis: a national population-based study","volume":"35","author":"Shen","year":"2012","journal-title":"Diabetes Care"},{"key":"B33","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1053\/j.gastro.2024.02.052","article-title":"Diagnosis and management of acute pancreatitis","volume":"167","author":"Trikudanathan","year":"2024","journal-title":"Gastroenterology"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1007\/s40265-022-01766-4","article-title":"Acute pancreatitis: diagnosis and treatment","volume":"82","author":"Szatmary","year":"2022","journal-title":"Drugs"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1053\/j.gastro.2018.01.032","article-title":"American Gastroenterological association institute guideline on initial management of acute pancreatitis","volume":"154","author":"Crockett","year":"2018","journal-title":"Gastroenterology"},{"key":"B36","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1016\/j.pan.2021.06.003","article-title":"Glucose levels show independent and dose-dependent association with worsening acute pancreatitis outcomes: post-hoc analysis of a prospective, international cohort of 2250 acute pancreatitis cases","volume":"21","author":"Nagy","year":"2021","journal-title":"Pancreatology"},{"key":"B37","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/s0140-6736(20)30539-0","article-title":"Urgent endoscopic retrograde cholangiopancreatography with sphincterotomy versus conservative treatment in predicted severe acute gallstone pancreatitis (apec): a multicentre randomised controlled trial","volume":"396","author":"Schepers","year":"2020","journal-title":"Lancet (London, England)"},{"key":"B38","doi-asserted-by":"publisher","first-page":"1974","DOI":"10.3390\/diagnostics12081974","article-title":"Diagnosis and treatment of acute pancreatitis","volume":"12","author":"Walkowska","year":"2022","journal-title":"Diagnostics (Basel, Switzerland)"},{"key":"B39","doi-asserted-by":"publisher","first-page":"773","DOI":"10.4143\/crt.2020.974","article-title":"Lasso-based machine learning algorithm for prediction of lymph node metastasis in t1 colorectal cancer","volume":"53","author":"Kang","year":"2021","journal-title":"Cancer Res Treat"},{"key":"B40","doi-asserted-by":"publisher","first-page":"107161","DOI":"10.1016\/j.cmpb.2022.107161","article-title":"Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011\u20132022)","volume":"226","author":"Loh","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"key":"B41","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1016\/j.chest.2023.06.038","article-title":"Methodologic issues specific to prediction model development and evaluation","volume":"164","author":"Jin","year":"2023","journal-title":"Chest"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1753529\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T14:12:38Z","timestamp":1773756758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1753529\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,17]]},"references-count":41,"alternative-id":["10.3389\/fdgth.2026.1753529"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2026.1753529","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,17]]},"article-number":"1753529"}}