Published online Jun 8, 2023. doi: 10.35712/aig.v4.i1.1
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
Processing time: 149 Days and 15.4 Hours
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.