%0 Thesis
%9 Doctoral
%A Farrington, Joseph Marc
%B Institute of Health Informatics
%D 2025
%F discovery:10204427
%I UCL (University College London)
%P 432
%T Machine learning and GPU-accelerated computing applied to platelet inventory management in a hospital blood bank
%U https://discovery.ucl.ac.uk/id/eprint/10204427/
%X Hospital blood banks must maintain an adequate stock of platelets and other blood  products to ensure that patients can receive transfusions when needed, while trying  to minimize wastage. Challenges include short product shelf lives, donor-recipient  compatibility, and the fact that some requested units may not be transfused.  I used machine learning (ML) and graphics processing unit (GPU)-accelerated  computing methods to find policies for two key decisions: how many platelet units  to order from a supplier (replenishment) and selecting platelet units to meet demand  (issuing).  My review of the literature identified opportunities to: use deep reinforcement  learning (DRL) to learn how to act in the large state spaces needed to represent  perishable stock; use GPU hardware to compute the optimal replenishment policy  for larger, more realistic problems; and improve issuing policies which have  received less attention than replenishment decisions.  I first developed a novel reinforcement learning environment to model a  simple platelet replenishment problem. DRL policies performed near-optimally on  simulated data and outperformed commonly used heuristic policies on real demand  trajectories from a UK hospital.  My GPU-accelerated implementation of value iteration enabled the optimal  policy to be computed for perishable inventory management problems where this  was recently deemed infeasible or impractical, with up to 16.8M states, using  consumer-grade hardware. These results can support benchmarking approximate  approaches including DRL.  I designed a novel ML-guided issuing policy to address the fact that not all requested platelet units are transfused. I explored how the utility of the policy  depended on the quality of patient-level ML predictions of transfusion and trained  an ML model, with AUROC 0.74, sufficiently good to support wastage reductions  under the new policy.  Finally, I extended the problem to jointly optimize replenishment and issuing  policies to manage multiple perishable products with substitution, including  platelets with all eight ABO/RhD blood types.
%Z Copyright © The Author 2025.  Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/).  Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms.  Access may initially be restricted at the author’s request.