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Demand forecasting for platelet usage: From univariate time series to multivariable models

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  • Maryam Motamedi
  • Jessica Dawson
  • Na Li
  • Douglas G Down
  • Nancy M Heddle

Abstract

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, five different demand forecasting methods, ARIMA (Auto Regressive Integrated Moving Average), Prophet, lasso regression (least absolute shrinkage and selection operator), random forest, and LSTM (Long Short-Term Memory) networks are utilized and evaluated via a rolling window method. 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 machine learning techniques for platelet transfusion using clinical predictors and with different amounts of data. We find that the multivariable approaches have the highest accuracy in general, however, if sufficient data are available, a simpler time series approach appears to be sufficient. We also comment on the approach to choose predictors for the multivariable models.

Suggested Citation

  • Maryam Motamedi & Jessica Dawson & Na Li & Douglas G Down & Nancy M Heddle, 2024. "Demand forecasting for platelet usage: From univariate time series to multivariable models," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-30, April.
  • Handle: RePEc:plo:pone00:0297391
    DOI: 10.1371/journal.pone.0297391
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    References listed on IDEAS

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    1. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of the Augmented Dickey-Fuller Test," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 277-280, July.
    2. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    3. Twumasi, Clement & Twumasi, Juliet, 2022. "Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1258-1277.
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    1. Maryam Motamedi & Jessica Dawson & Na Li & Douglas Down, 2025. "Blood platelet inventory management: Incorporating data-driven demand forecasts," Health Care Management Science, Springer, vol. 28(2), pages 191-206, June.

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