AI Case Study
Scientists at the Francis Crick Institute outperform medical models at predicting risk of death in patients with heart disease using machine learning
Scientists at the Francis Crick Institute, the Farr Institute of Health Informatics Research and University College London Hospitals NHS Foundation Trust have leveraged machine learning to develop a model to predict risk of death from coronary artery disease. The model was trained on electronic health data of over 80,000 patients and outperformed expert-designed models at predicting patient mortality.
Public And Social Sector
Education And Academia
"The model was designed using the electronic health data of over 80,000 patients, collected as part of routine care, and available for researchers on the CALIBER platform.
Scientists at the Crick, working collaboratively with colleagues at the Farr Institute of Health Informatics Research and University College London Hospitals NHS Foundation Trust, wanted to see if they could create a model for coronary artery disease -- the leading cause of death in the UK -- that outperforms experts using self-taught machine learning techniques.
An expert-constructed prognostic model for coronary artery disease which this work was compared against made predictions based on 27 variables chosen by medical experts, such as age, gender and chest pains. By contrast, the Crick team got their AI algorithms to train themselves, searching for patterns and picking the most relevant variables from a set of 600.
This study was a proof-of-principle to compare expert-designed models to machine learning approaches, but a similar model could be implemented in the clinic in the not too distant future."
R And D
Core Research And Development
"Coronary artery disease develops when the major blood vessels that supply the heart with blood, oxygen and nutrients become damaged, or narrowed by fatty deposits. Eventually restricted blood flow to the heart can lead to chest pain and shortness of breath, while a complete blockage can cause a heart attack."
"Not only did the new data-driven model beat expert-designed models at predicting patient mortality, but it also identified new variables that doctors hadn't thought of."
electronic health data of over 80,000 patients