Bid farewell to single indicators: Machine learning models integrating multidimensional data lead thrombosis risk prediction into a new stage

doi:10.3748/wjg.118337

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

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

Core Tip

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.