Publication Name	World Journal of Gastroenterology
Manuscript ID	118337
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Editorial
Article Title	Bid farewell to single indicators: Machine learning models integrating multidimensional data lead thrombosis risk prediction into a new stage
Manuscript Source	Invited Manuscript
All Author List	Ying Su, Dong-Xia Wang, Yi-Qun Zhao and Xue Xing
Funding Agency and Grant Number
Corresponding Author	Xue Xing, Doctorate Student, Department of Clinical Laboratory, The Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Dalian 116021, Liaoning Province, China. dyeyxx39198645@126.com
Key Words	Nonvariceal gastrointestinal bleeding; Thromboembolism; Machine learning; Risk prediction; Precision medicine; Classification boosting algorithm; D-dimer
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.
Citation	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; In press

Received	2025-12-30 06:04
Peer-Review Started	2025-12-30 06:05
First Decision by Editorial Office Director	2026-01-29 09:21
Return for Revision	2026-01-29 09:21
Revised	2026-02-02 02:17
Publication Fee Transferred
Second Decision by Editor	2026-02-11 02:38
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-02-12 08:56
Articles in Press	2026-02-12 08:56
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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