Yuan Q, Zhao WL, Qin B. Big data and variceal rebleeding prediction in cirrhosis patients. Artif Intell Gastroenterol 2023; 4(1): 1-9 [DOI: 10.35712/aig.v4.i1.1]
Corresponding Author of This Article
Bo Qin, MD, Professor, Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400042, China. qinbo@cqmu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Gastroenterol. Jun 8, 2023; 4(1): 1-9 Published online Jun 8, 2023. doi: 10.35712/aig.v4.i1.1
Big data and variceal rebleeding prediction in cirrhosis patients
Quan Yuan, Wen-Long Zhao, Bo Qin
Quan Yuan, Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
Wen-Long Zhao, College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
Wen-Long Zhao, Medical Data Science Academy, Chongqing 400016, China
Wen-Long Zhao, Chongqing Engineering Research Centre for Clinical Big-data and Drug Evaluation, Chongqing 400016, China
Bo Qin, Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
Author contributions: Yuan Q selected the topic and performed the majority of conception, writing, and revision of the manuscript; Zhao WL provided think tank, platform with regard to big data, site for academic discussion, and revision suggestions for the manuscript; Qin B provided administrative help and was the instigator and coordinator of the study; All authors have read and approved the final manuscript.
Conflict-of-interest statement: All the authors declare that they have no conflicts of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bo Qin, MD, Professor, Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400042, China. qinbo@cqmu.edu.cn
Received: January 8, 2023 Peer-review started: January 8, 2023 First decision: January 21, 2023 Revised: February 3, 2023 Accepted: March 10, 2023 Article in press: March 10, 2023 Published online: June 8, 2023
Abstract
Big data has convincing merits in developing risk stratification strategies for diseases. The 6 “V”s of big data, namely, volume, velocity, variety, veracity, value, and variability, have shown promise for real-world scenarios. Big data can be applied to analyze health data and advance research in preclinical biology, medicine, and especially disease initiation, development, and control. A study design comprises data selection, inclusion and exclusion criteria, standard confirmation and cohort establishment, follow-up strategy, and events of interest. The development and efficiency verification of a prognosis model consists of deciding the data source, taking previous models as references while selecting candidate predictors, assessing model performance, choosing appropriate statistical methods, and model optimization. The model should be able to inform disease development and outcomes, such as predicting variceal rebleeding in patients with cirrhosis. Our work has merits beyond those of other colleagues with respect to cirrhosis patient screening and data source regarding variceal bleeding.
Core Tip: Big data have been applied in many fields including finance, traffic control, logistics, healthcare, and environmental protection. Modeling is an efficient method for completing various tasks, and verification of its validity is vital for ensuring high-quality operation and yielding satisfactory results. Predictor screening guarantees the establishment of a practical, convenient, and favorable model for prognosis prediction. Utilizing a regression model trained with numerous data mined from big data acquired from real-world hospitals is helpful for informing disease or status onset and its prognosis such as in variceal rebleeding, which is one of the leading causes of death in cirrhosis patients.
