eprintid: 10204427 rev_number: 8 eprint_status: archive userid: 699 dir: disk0/10/20/44/27 datestamp: 2025-03-06 15:54:57 lastmod: 2025-03-06 15:54:57 status_changed: 2025-03-06 15:54:57 type: thesis metadata_visibility: show sword_depositor: 699 creators_name: Farrington, Joseph Marc title: Machine learning and GPU-accelerated computing applied to platelet inventory management in a hospital blood bank ispublished: unpub divisions: UCL divisions: B02 divisions: DD4 note: 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. abstract: 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. date: 2025-02-28 date_type: published oa_status: green full_text_type: other thesis_class: doctoral_open thesis_award: Ph.D language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2358882 lyricists_name: Farrington, Joseph lyricists_id: JMFAR78 actors_name: Farrington, Joseph actors_name: Zahnhausen-Stuber, Petra actors_id: JMFAR78 actors_id: PMZAH20 actors_role: owner actors_role: impersonator full_text_status: public pagerange: 1-432 pages: 432 institution: UCL (University College London) department: Institute of Health Informatics thesis_type: Doctoral citation: Farrington, Joseph Marc; (2025) Machine learning and GPU-accelerated computing applied to platelet inventory management in a hospital blood bank. Doctoral thesis (Ph.D), UCL (University College London). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10204427/1/Farrington_thesis.pdf