Machine learning in predicting postoperative complications in Crohn's disease

被引:0
作者
Zhang, Li-Fan [1 ,2 ]
Chen, Liu-Xiang [1 ,2 ]
Yang, Wen-Juan [1 ,2 ]
Hu, Bing [1 ,2 ]
机构
[1] Sichuan Univ, West China Hosp, Dept Gastroenterol & Hepatol, 37 Guoxue Alley, Chengdu 610041, Sichuan, Peoples R China
[2] Sichuan Univ, West China Hosp, Digest Endoscopy Med Engn Res Lab, Chengdu 610041, Sichuan, Peoples R China
来源
WORLD JOURNAL OF GASTROINTESTINAL SURGERY | 2024年 / 16卷 / 08期
关键词
Crohn's disease; Intestinal resection; Postoperative complications; Machine learning; Explainability;
D O I
10.4240/wjgs.v16.i8.2745
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Crohn's disease (CD) is a chronic inflammatory bowel disease of unknown origin that can cause significant disability and morbidity with its progression. Due to the unique nature of CD, surgery is often necessary for many patients during their lifetime, and the incidence of postoperative complications is high, which can affect the prognosis of patients. Therefore, it is essential to identify and manage postoperative complications. Machine learning (ML) has become increasingly important in the medical field, and ML-based models can be used to predict postoperative complications of intestinal resection for CD. Recently, a valuable article titled "Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study" was published by Wang et al. We appreciate the authors' creative work, and we are willing to share our views and discuss them with the authors.
引用
收藏
页数:4
相关论文
共 11 条
[1]  
BEST WR, 1976, GASTROENTEROLOGY, V70, P439
[2]   A Model-Agnostic Feature Attribution Approach to Magnetoencephalography Predictions Based on Shapley Value [J].
Fan, Yongdong ;
Mao, Haokun ;
Li, Qiong .
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2023, 27 (05) :2524-2535
[3]   Selective oversampling approach for strongly imbalanced data [J].
Gnip, Peter ;
Vokorokos, Liberios ;
Drotar, Peter .
PEERJ COMPUTER SCIENCE, 2021,
[4]   Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience [J].
Goodwin, Nastacia L. ;
Nilsson, Simon R. O. ;
Choong, Jia Jie ;
Golden, Sam A. .
CURRENT OPINION IN NEUROBIOLOGY, 2022, 73
[5]   Semantic-Guided Class-Imbalance Learning Model for Zero-Shot Image Classification [J].
Ji, Zhong ;
Yu, Xuejie ;
Yu, Yunlong ;
Pang, Yanwei ;
Zhang, Zhongfei .
IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (07) :6543-6554
[6]   Bayes Imbalance Impact Index: A Measure of Class Imbalanced Data Set for Classification Problem [J].
Lu, Yang ;
Cheung, Yiu-Ming ;
Tang, Yuan Yan .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 31 (09) :3525-3539
[7]   Gender imbalance in pediatric palliative care research samples [J].
Macdonald, Mary Ellen ;
Chilibeck, Gillian ;
Affleck, William ;
Cadell, Susan .
PALLIATIVE MEDICINE, 2010, 24 (04) :435-444
[8]   Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based COMMENT [J].
McCoy, Liam G. ;
Brenna, Connor T. A. ;
Chen, Stacy S. ;
Vold, Karina ;
Das, Sunit .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 2022, 142 :252-257
[9]   Crohn's disease [J].
Torres, Joana ;
Mehandru, Saurabh ;
Colombel, Jean-Frederic ;
Peyrin-Biroulet, Laurent .
LANCET, 2017, 389 (10080) :1741-1755
[10]   Oversampling in Health Surveys: Why, When, and How? [J].
Vaughan, Roger .
AMERICAN JOURNAL OF PUBLIC HEALTH, 2017, 107 (08) :1214-1215