Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

Journal: World journal of gastroenterology

Published Date: Jun 21, 2024

Abstract

BACKGROUND: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.

Authors

Li-Qun Cai

Department of Colorectal and Anal Surgery, Whenzhou Central Hospital, Wenzhou 325000, Zhejiang Province, China.
Da-Qing Yang

Department of Colorectal and Anal Surgery, Whenzhou Central Hospital, Wenzhou 325000, Zhejiang Province, China.
Rong-Jian Wang

Department of Colorectal and Anal Surgery, Whenzhou Central Hospital, Wenzhou 325000, Zhejiang Province, China.
He Huang
Yi-Xiong Shi

Department of Colorectal and Anorectal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang Province, China. danshiyixiong@163.com.

Keywords

Adult Aged China Colorectal Neoplasms Female Humans Machine Learning Male Middle Aged Nomograms Postoperative Complications Reoperation Retrospective Studies Risk Assessment Risk Factors ROC Curve

External Resources

View on PubMed Access via DOI PubMed (38946868)

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

Abstract

Authors

Keywords

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