AI-enabled Blood Transfusion System Student

Lead Research Organisation: University College London
Department Name: Institute of Health Informatics

Abstract

Supervisors: Dr. Wai Keong Wong (Consultant Haematologist and Chief Research Information Officer, UCLH), Dr. Ken (Kezhi) Li (Lecturer, Institute of Health Informatics, UCL)

Blood products are central to many aspects of modern medical care. Ordering and supply of both red cell and platelet units are complicated by the requirement to use specific blood-group or platelet-group types. This is currently carried out by local experts using crude estimates, which leads to over or under ordering of specific blood groups to constitute stock. Patient care may suffer if blood products are not available and treatment is delayed or cancelled if products are not available.

In this project, we will explore various AI methods to build models of making accurate predictions of the blood product usage by learning from local experts and using actual data from NHS and UCLH. Based on the prediction model, optimal approaches of blood products ordering system can be developed and implemented in the long run. Specifically, integration of hospital and laboratory data can define a practical model that can help to order and reduce wastage, and it would be much more powerful if real-time data and deep learning techniques for prediction of demand are utilized. Furthermore, the AI models of blood products recommendation will be evaluated prospectively on the actual retrospective use at UCLH. The application of this technology at scale, would reduce blood wastage and reduce inappropriate use of universal donor blood. We believe that this project fits with the goals of the CDT as it uses applies AI to manage what is a scarce resource (donations from voluntary donors) to match against the transfusion need for patients. This is a unique collaboration between the national blood service (NHSBT), an NHS trust (UCLH) with a fully integrated comprehensive EHR (Epic) and a maturing research platform (EMAP).

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S021612/1 01/04/2019 30/09/2027
2247846 Studentship EP/S021612/1 01/10/2019 30/09/2023 Joseph Farrington