{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T10:53:03Z","timestamp":1791543183372,"version":"4.3.4"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"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. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early identification of phenotype-stratified, short-term complication risk remains a critical unmet need in Crohn\u2019s disease (CD). Existing models focus on single outcomes and lack interpretability, limiting clinical use. We developed a clinically interpretable machine-learning framework to enable Montreal phenotype\u2013stratified prediction of three one-year complication categories: bowel resection (Task 1), perianal complications (Task 2), and abdominal complications (Task 3).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from 370 patients across two clinical centers were pooled for model development and evaluated using stratified 10-fold cross-validation. Within each fold, data preprocessing, LASSO feature selection, SMOTE-Tomek resampling, model optimization, calibration, and threshold determination were restricted to the training data. Seven machine-learning algorithms were compared in terms of discrimination, calibration, with decision-curve analysis used to explore potential net benefit. SHapley Additive exPlanations (SHAP) were used for model interpretation, and nomograms, heatmaps, and a web-based calculator were generated as exploratory visualizations of model-derived risk estimates rather than as clinical decision-support tools. Phenotype-stratified and non-linear associations between CDAI and complication risk were further analyzed within the Montreal framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Within one year after discharge, bowel resection, perianal complications, and abdominal complications occurred in 70 (18.9%), 142 (38.4%), and 84 (22.7%) patients, respectively. Random Forest showed the most consistent overall performance, achieving an AUROC of 0.86 (95% CI, 0.82\u20130.90) for bowel resection, 0.71 (95% CI, 0.64\u20130.78) for perianal complications, and 0.73 (95% CI, 0.69\u20130.78) for abdominal complications. SHAP identified distinct predictors across outcomes: disease behavior, nutritional support therapy, and disease duration for bowel resection; prior perianal complications, extraintestinal manifestations, and CDAI for perianal complications; and prior abdominal complications, nutritional support therapy, and Montreal L3 phenotype for abdominal complications. Receipt of NST was interpreted as a clinical marker of nutritional depletion and greater disease burden rather than as a causal risk factor. Montreal-based stratification revealed marked heterogeneity, with isolated small-bowel disease associated with higher risks of bowel resection and abdominal complications, and isolated colonic disease with the highest burden of perianal complications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The interpretable RF\u2013SHAP framework showed potential for Montreal phenotype\u2013stratified prediction of one-year complication risk in CD and inform future evaluation of risk stratification and phenotype-informed management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2026.1902465","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T04:27:20Z","timestamp":1789446440000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable random forest\u2013SHAP framework for Montreal phenotype\u2013stratified prediction of one-year complications in Crohn\u2019s disease"],"prefix":"10.3389","volume":"17","author":[{"given":"Kun","family":"Xia","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, The National Key Clinical Specialty, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, Fujian, China"]},{"name":"Department of Health Technology and Informatics, The Hong Kong PolytechnicUniversity","place":["Hong Kong, Hong Kong SAR, China"]},{"name":"Clinical Research Center for Gut Microbiota and Digestive Diseases of Fujian Province, Xiamen Key Laboratory of Intestinal Microbiome and Human Health","place":["Xiamen, Fujian, China"]},{"name":"Department of Digestive Disease, Institute for Microbial Ecology, Xiamen University","place":["Xiamen, Fujian, 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National Key Clinical Specialty, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, Fujian, China"]},{"name":"Clinical Research Center for Gut Microbiota and Digestive Diseases of Fujian Province, Xiamen Key Laboratory of Intestinal Microbiome and Human Health","place":["Xiamen, Fujian, China"]},{"name":"Department of Digestive Disease, Institute for Microbial Ecology, Xiamen University","place":["Xiamen, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingan","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Pathology, TangShan GongRen Hospital","place":["Tangshan, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongzhi","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The National Key Clinical Specialty, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, 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