Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Apr 27, 2021
Date Accepted: Nov 21, 2021
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Reduction of Platelet Outdating and Shortage by Forecasting Demand with Statistical Learning and Deep Neural Networks: Modeling Study
ABSTRACT
Background:
Platelets are a valuable and perishable blood product. Managing platelet inventory is a demanding task due to 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 deep neural network. In addition, we aim to calculate possible reduction in waste and shortage of platelets under use of the predictions in a retrospective simulation of the platelet inventory.
Methods:
Predictions of daily platelet demand are made by a LASSO (Least Absolute Shrinkage and Selection Operator) model and by a recurrent neural network (RNN) with Long-short Term Memory (LSTM), respectively. Both models use the same set of 81 clinical features. Predictions are passed to a simulation of the blood inventory to calculate possible reduction of waste and shortage as compared to historical data.
Results:
From January 01, 2008 to December 31, 2018 the average 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 5.0 % with the RNN while shortages were 2.1 % and 1.7 % with LASSO and RNN, respectively. Daily predictions of platelet for the next 2 and 4 days had mean average percentage errors of 25.5 % and 18.1 % with the LASSO and 26.3 % and 19.2 % with the RNN, respectively.
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 250,000 dollar per year.
Citation
