How the predictions of algorithms used in healthcare provision change: an investigation using COVID-19 and Cardiovascular disease risk prediction case studies.

Date of Approval
Application Number
Technical Summary

Artificial Intelligence (AI) and Machine Learning (ML) techniques can find and interpret patterns in data that people find difficult to reliably detect. They are starting to be applied to medical systems. A major advantage of these approaches is their ability to learn, updating their estimates in response to new information. However, that flexibility also poses a problem for regulation as it is important to ensure that any changes do not change the benefit-risk ratio in a way that poses risks to patient safety.

This project is applying AI techniques to subsets of CPRD Aurum primary care data in order to estimate risks associated with COVID and cardiovascular disease (CVD). It will fit models to initial datasets, then refit the models after adding a block of more recent data. The situation around COVID has changed rapidly, while that for CVD is likely to be more stable, so these represent two different scenarios of interest. Four types of models (Logistic Regression, Bayesian networks, Neural networks, and Random Forest tree-based models) will be investigated. Changes in how well the models fit the data, the models' internal structure, their parameter estimates and associated uncertainties, and their predictions will be examined. The aim is to understand the relative stability and informativeness of each of these measures, in order to develop methodology to determine if there has been a significant change in the way that an algorithm is working to inform regulators of the need for re-assessment.

This work will benefit patients, including NHS patients in England and Wales, by informing the development of regulatory standards for AI algorithms used in diagnostic systems and other medical devices, particularly in the area of Adaptive AI software programmes which can learn and change as they receive new information.

Health Outcomes to be Measured

Positive COVID test result; cardiovascular event (stroke/heart attack);hospitalisation/death.


Mike Lonergan - Chief Investigator - CPRD
Mike Lonergan - Corresponding Applicant - CPRD
Allan Tucker - Collaborator - Brunel University
Puja Myles - Collaborator - CPRD
Ylenia Rotalinti - Collaborator - Brunel University


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