  Platelet products are both expensive and have very short shelf lives. As
usage rates for platelets are highly variable, the effective management of
platelet demand and supply is very important yet challenging. The primary goal
of this paper is to present an efficient forecasting model for platelet demand
at Canadian Blood Services (CBS). To accomplish this goal, four different
demand forecasting methods, ARIMA (Auto Regressive Moving Average), Prophet,
lasso regression (least absolute shrinkage and selection operator) and LSTM
(Long Short-Term Memory) networks are utilized and evaluated. We use a large
clinical dataset for a centralized blood distribution centre for four hospitals
in Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily
platelet transfusions along with information such as the product
specifications, the recipients' characteristics, and the recipients' laboratory
test results. This study is the first to utilize different methods from
statistical time series models to data-driven regression and a machine learning
technique for platelet transfusion using clinical predictors and with different
amounts of data. We find that the multivariate approaches have the highest
accuracy in general, however, if sufficient data are available, a simpler time
series approach such as ARIMA appears to be sufficient. We also comment on the
approach to choose clinical indicators (inputs) for the multivariate models.
