- New Artificial Intelligence model developed to predict premature deaths due to chronic disease.
- The Computer model used ‘machine learning’ algorithms for prediction
- For the study, the team included over half a million people aged between 40 and 69.
Scientists at the University of Nottingham published a study that says computers are capable of predicting premature death due to chronic disease.
The new computer model is based on Artificial Intelligence that is using ‘machine learning’ algorithms to predict the risk of early death due to chronic disease in a large middle-aged population.
The study, published by PLOS ONE journal, found that the new AI Machine Learning models known as “random forest” and “deep learning” were very accurate in its predictions and performed better than the current standard approach to prediction developed by human experts.
“Preventative healthcare is a growing priority in the fight against serious diseases so we have been working for a number of years to improve the accuracy of computerised health risk assessment in the general population. 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 Assistant Professor of Epidemiology and Data Science, Dr. Stephen Weng, who is leading the research.
“We mapped the resulting predictions to mortality data from the cohort, using Office of National Statistics death records, the UK cancer registry and ‘hospital episodes’ statistics. We found machine learned algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert.” — Weng added.
“There is currently intense interest in the potential to use ‘AI’ or ‘machine-learning’ to better predict health outcomes. In some situations we may find it helps, in others it may not. In this particular case, we have shown that with careful tuning, these algorithms can usefully improve prediction.” Professor Joe Kai, one of the clinical academics working on the project said.
“These techniques can be new to many in health research, and difficult to follow. We believe that by clearly reporting these methods in a transparent way, this could help with scientific verification and future development of this exciting field for health care.”
For the study, the team included over half a million people aged between 40 and 69.
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—found to be the least accurate at predicting mortality—and also a multivariate Cox model which worked better but tended to over-predict risk.
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