Computers which are capable of
teaching themselves to predict premature death could greatly improve
preventative healthcare in the future, suggests a new study by experts at the
University of Nottingham. The team of healthcare data scientists and doctors
have developed and tested a system of computer-based 'machine learning'
algorithms to predict the risk of early death due to chronic disease in a large
middle-aged population. They found this AI system was very accurate in its
predictions and performed better than the current standard approach to
prediction developed by human experts. The team used health data from just over
half a million people aged between 40 and 69 recruited to the UK Biobank
between 2006 and 2010 and followed up until 2016.
Researchers 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. This uses
computers to build new risk prediction models that take into account 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. They 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. They found machine learned
algorithms were significantly more accurate in predicting death than the
standard prediction models developed by a human expert.
More information: