Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study

Maximilian Schilling; Lennart Rickmann; Gabriele Hutschenreuter; Cord Spreckelsen

doi:10.2196/29978

Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study

JMIR Med Inform. 2022 Feb 1;10(2):e29978. doi: 10.2196/29978.

Authors

Maximilian Schilling¹, Lennart Rickmann¹, Gabriele Hutschenreuter², Cord Spreckelsen³

Affiliations

¹ Institute for Medical Informatics, University Hospital Aachen, RWTH Aachen University, Aachen, Germany.
² Institute for Transfusion Medicine, University Hospital Aachen, RWTH Aachen University, Aachen, Germany.
³ Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Friedrich Schiller University, Jena, Germany.

PMID: 35103612
PMCID: PMC8848235
DOI: 10.2196/29978

Abstract

Background: Platelets are a valuable and perishable blood product. Managing platelet inventory is a demanding task because of short shelf lives and high variation in daily platelet use patterns. Predicting platelet demand is a promising step toward avoiding obsolescence and shortages and ensuring optimal care.

Objective: The aim of this study is to forecast platelet demand for a given hospital using both a statistical model and a deep neural network. In addition, we aim to calculate the possible reduction in waste and shortage of platelets using said predictions in a retrospective simulation of the platelet inventory.

Methods: Predictions of daily platelet demand were made by a least absolute shrinkage and selection operator (LASSO) model and a recurrent neural network (RNN) with long short-term memory (LSTM). Both models used the same set of 81 clinical features. Predictions were passed to a simulation of the blood inventory to calculate the possible reduction in waste and shortage as compared with historical data.

Results: From January 1, 2008, to December 31, 2018, the waste and shortage rates for platelets were 10.1% and 6.5%, respectively. In simulations of platelet inventory, waste could be lowered to 4.9% with the LASSO and 5% with the RNN, whereas shortages were 2.1% and 1.7% with the LASSO and RNN, respectively. Daily predictions of platelet demand for the next 2 days had mean absolute percent errors of 25.5% (95% CI 24.6%-26.6%) with the LASSO and 26.3% (95% CI 25.3%-27.4%) with the LSTM (P=.01). Predictions for the next 4 days had mean absolute percent errors of 18.1% (95% CI 17.6%-18.6%) with the LASSO and 19.2% (95% CI 18.6%-19.8%) with the LSTM (P<.001).

Conclusions: Both models allow for predictions of platelet demand with similar and sufficient accuracy to significantly reduce waste and shortage in a retrospective simulation study. The possible improvements in platelet inventory management are roughly equivalent to US $250,000 per year.

Keywords: LASSO; LSTM; blood inventory management; deep learning; demand forecasting; platelets; statistical learning; time series forecasting.

©Maximilian Schilling, Lennart Rickmann, Gabriele Hutschenreuter, Cord Spreckelsen. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 01.02.2022.