A robust autonomous method for blood demand forecasting.

ABSTRACT:

Background

Blood supply chain management requires estimates about the demand of blood products. The more accurate these estimates are, the less wastage and fewer shortages occur. While the current literature demonstrates tangible benefits from statistical forecasting approaches, it highlights issues that discourage their use in blood supply chain optimization: there is no single approach that works everywhere, and there are no guarantees that any favorable method performance continues into the future.

Study design and methods

We design a novel autonomous forecasting system to solve the aforementioned issues. We show how possible changes in blood demand could affect prediction performance using partly synthetic demand data. We use these data then to investigate the performanc

SUBMITTER: Turkulainen EV 

PROVIDER: S-EPMC9325496 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature