Study: AI predicts risk of death from heart disease more accurately than experts
Scientists have designed a model using Artificial Intelligence that can predict risk of death in patients with coronary heart disease (CHD) better than expert-constructed models.
According to a new study published in PLOS One, researchers from the Francis Crick Institute, working with University College London Hospitals NHS Foundation Trust and the Farr Institute of Health Informatics Research, developed the AI model using the data of 80,000 patients, available for researchers through UCL’s CALIBER platform, which links four sources of electronic health data in England.
The model that the AI one was compared to made predictions based on 27 variables chosen by medical experts, while the Crick team got their AI algorithms to train themselves, look for patterns and select the most relevant variables from a set of 600.
Both machine learning and AI are picking up steam in healthcare, with hospitals testing or deploying the tech for a range of use cases from treating patients with pancreatic cancer to reducing surgical site infections while experts are saying the next generation of clinical decision support tools will include AI in workflow to improve diagnostics, imaging, radiology and pathology, among other functions.
Consultancy McKinsey said last month that hospitals need a solid digital base comprising a modern infrastructure with cloud, mobile and web capabilities in place before starting down the road to AI and machine learning.
Research such as this points to opportunities for improving care delivery and, in certain cases, reducing costs. The paper in PLOS One showed that the model could pick out prognostic factors that may be unlikely to be considered by an expert-constructed model — such as home visits, which could indicate frailty — as they are not ‘obviously’ linked to risk of death in heart disease patients, the scientists said.
“What we've done here is a proof of principle — we've shown that models where computers pick the variables can do as well, or even slightly better than traditional ones where variables are picked by experts,” said Andrew Steele, first author of the research article, who carried out the project at the Crick Bioinformatics and Computational Biology Laboratory.
While conventional models are more readily interpretable, they also “require significant expert input to construct, potentially not making use of the richness of available data”, Steele added, while data-driven modelling can simplify the process and allow novel variables to be identified.
“Doctors already use the kind of traditional models we compared our machine learning approaches to in order to work out whether a patient is at risk of, for example, a heart attack,” Steele said. “We need more work developing a robust framework for models like these, and in particular for gaining access to the data — but, once those are in place, these kinds of machine learning tools are going to start making their way into clinical practice.”
As that happens, scientists believe similar models could be used in the future to predict risk and suggest treatments, helping doctors with more and more aspects of their practice.
“In the longer term, I think models like these will go from predicting risk to suggesting treatments, based on what has been most successful in other patients with similar characteristics,” Steele said. "Together with things like image recognition technologies helping with interpretation of scans, I think doctors are going to be using models to help with more and more aspects of their practice.”
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