Machine learning model predicting factors for incisional infection following right hemicolectomy for colon cancer

• Published in: BMC surgery Vol. 24; no. 1; pp. 279 - 9
• Main Authors: Li, Jiatong, Yan, Zhaopeng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01.10.2024; BioMed Central; BMC
• Subjects: Accuracy; Aged; Body mass index; Body size; Cancer; Cancer surgery; Cancer therapies; Chemotherapy; Colectomy - adverse effects; Colon; Colon cancer; Colonic Neoplasms - surgery; Colorectal cancer; Colorectal carcinoma; Complications; Data collection; Decision trees; Deep learning; Demographic variables; Demographics; Feature selection; Female; Health aspects; Health services; Hospitals; Humans; Incisional infection; Infection; Laparoscopy; Learning algorithms; Logistic Models; Logistics; Machine Learning; Machine learning models; Male; Malignancy; Medical records; Medical research; Medicine, Experimental; Middle Aged; Multidisciplinary teams; Patients; Performance prediction; Perioperative care; Postoperative; Radiation therapy; Random variables; Regression analysis; Regression models; Retrospective Studies; Right hemicolectomy; Risk assessment; Risk Factors; Support Vector Machine; Support vector machines; Surgical site infections; Surgical Wound Infection - diagnosis; Surgical Wound Infection - epidemiology; Surgical Wound Infection - etiology; Sutures; Taiwan; China; Deep learning; Machine learning models; Right hemicolectomy; Colorectal cancer; Incisional infection

Colorectal cancer is a prevalent malignancy worldwide, and right hemicolectomy is a common surgical procedure for its treatment. However, postoperative incisional infections remain a significant complication, leading to prolonged hospital stays, increased healthcare costs, and patient discomfort. Th...