These were pitched against the traditionally used ‘Cox regression’ prediction model based on age and gender, which was found to be the least accurate at predicting mortality, and also against a multivariate Cox model, which worked better but tended to over-predict risk.
Scientists have developed and tested an artificial intelligence (AI)-based computer system to predict the risk of early death due to chronic diseases in a largely middle-aged population. The computer-based ‘machine learning’ algorithm system was very accurate in its predictions and performed better than the current standard approach to prediction developed by human experts, according to the study published in the journal PLOS ONE.
Researchers at the University of Nottingham in the UK used health data from over half a million people aged between 40 and 69 recruited to the UK Biobank between 2006 and 2010 and followed up until 2016. “Most applications focus on a single disease area, but predicting death due to several different disease outcomes is highly complex, especially given environmental and individual factors that may affect them,” said Stephen Weng, Assistant Professor at the University of Nottingham.
“We have taken a major step forward in this field by developing a unique and holistic approach to predicting a person’s risk of premature death by machine learning,” Weng said in a statement. “This uses computers to build new risk prediction models that consider a wide range of demographic, biometric, clinical, and lifestyle factors for each individual assessed, even their dietary consumption of fruit, vegetables, and meat per day,” he said.
The AI machine learning models used in the new study are known as ‘random forest’ and ‘deep learning’. These were pitched against the traditionally used ‘Cox regression’ prediction model based on age and gender, which was found to be the least accurate at predicting mortality. A multivariate Cox model also worked better but tended to over-predict risk.
“There is currently intense interest in the potential to use ‘AI’ or ‘machine learning’ to predict health outcomes better,” said Professor Joe Kai, one of the clinical academics working on the project. “In some situations, we may find it helps; in others, it may not. In this case, we have shown that these algorithms can usefully improve prediction with careful tuning,” Kai said.
