Development of blood demand prediction model using artificial intelligence based on national public big data.

ABSTRACT:

Objective

Modern healthcare systems face challenges related to the stable and sufficient blood supply of blood due to shortages. This study aimed to predict the monthly blood transfusion requirements in medical institutions using an artificial intelligence model based on national open big data related to transfusion.

Methods

Data regarding blood types and components in Korea from January 2010 to December 2021 were obtained from the Health Insurance Review and Assessment Service and Statistics Korea. The data were collected from a single medical institution. Using the obtained information, predictive models were developed, including eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and category boosting (CatBoost). An ensemble model was created usin

SUBMITTER: Kwon HJ 

PROVIDER: S-EPMC10798124 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature