Su Y, Wang DX, Zhao YQ, Xing X. Bid farewell to single indicators: Machine learning models integrating multidimensional data lead thrombosis risk prediction into a new stage. World J Gastroenterol 2026; 32(34): 118337 [DOI: 10.3748/wjg.118337]
Corresponding Author of This Article
Xue Xing, Department of Clinical Laboratory, The Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Dalian 116021, Liaoning Province, China. dyeyxx39198645@126.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
editorial
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Su Y, Wang DX, Zhao YQ, Xing X. Bid farewell to single indicators: Machine learning models integrating multidimensional data lead thrombosis risk prediction into a new stage. World J Gastroenterol 2026; 32(34): 118337 [DOI: 10.3748/wjg.118337]
World J Gastroenterol. Sep 14, 2026; 32(34): 118337 Published online Sep 14, 2026. doi: 10.3748/wjg.118337
Bid farewell to single indicators: Machine learning models integrating multidimensional data lead thrombosis risk prediction into a new stage
Ying Su, Dong-Xia Wang, Yi-Qun Zhao, Xue Xing
Ying Su, Dong-Xia Wang, Yi-Qun Zhao, Xue Xing, Department of Clinical Laboratory, The Second Affiliated Hospital of Dalian Medical University, Dalian 116021, Liaoning Province, China
Co-first authors: Ying Su and Dong-Xia Wang.
Co-corresponding authors: Yi-Qun Zhao and Xue Xing.
Author contributions: Su Y and Wang DX contributed equally as co-first authors; Xing X designed the overall concept and outline of the manuscript; Su Y contributed to the discussion and design of the manuscript; Wang DX and Zhao YQ contributed to the writing and editing the manuscript, illustrations, and review of literature; Zhao YQ and Xing X contributed equally as co-corresponding authors. All authors approved the final version to publish.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Xue Xing, Department of Clinical Laboratory, The Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Dalian 116021, Liaoning Province, China. dyeyxx39198645@126.com
Received: December 30, 2025 Revised: February 2, 2026 Accepted: February 12, 2026 Published online: September 14, 2026 Processing time: 232 Days and 14.8 Hours
Abstract
This editorial reviews a multicenter study published by Lu et al in the World Journal of Gastroenterology. The study innovatively applied five machine learning models to predict the risk of thromboembolic events in patients with non-variceal gastrointestinal bleeding. The research found that the simplified model constructed based on 10 key clinical variables (including D-dimer, age, anticoagulant history, etc.) significantly outperformed the traditional single indicator of D-dimer in predictive performance. Among them, the classification boosting algorithm model showed the best discrimination and calibration in external validation. This commentary delves into the milestone significance of the study in the field of gastroenterology risk stratification, pointing out that it marks a paradigm shift from reliance on a single biomarker to the integration of multidimensional clinical data. We further analyze the potential advantages of machine learning models compared to traditional scoring systems (such as the Padua score) and the challenges faced in clinical integration, emphasizing that it provides a powerful decision support tool for achieving personalized and precise thrombus prevention management for nonvariceal gastrointestinal bleeding patients, and looks forward to future research exploring prospective validation, algorithm interpretability, and clinical workflow integration.
Core Tip: This multicenter study demonstrates that machine learning models based on routine clinical data can effectively predict thrombotic risk in patients with nonvariceal gastrointestinal bleeding, outperforming the traditional D-dimer biomarker. The classification boosting algorithm model exhibited the best performance, aiding in the clinical identification of high-risk patients for early intervention while avoiding excessive monitoring in low-risk individuals, thereby advancing thromboprophylaxis strategies toward precision medicine.
