Key points from article :
Out of hospital cardiac arrest is common around the world, but associated with low rates of survival.
Machine learning, can accurately predict the risk of an out of hospital cardiac arrest.
Systems can learn from data and identify patterns to inform decisions with minimal intervention.
Researchers say, meteorological data are extensive and complex, and machine learning has the potential to pick up associations.
They assessed daily out-of-hospital cardiac arrest, using daily weather and timing data.
They carried out a 'heatmap analysis,' using another dataset drawn from the location of out of hospital cardiac arrests.
Results predicted that Sundays, Mondays, public holidays, winter, low temperatures were more strongly associated with cardiac arrest.
This predictive model may be useful for preventing and improving the prognosis of patients.
Research by National Cerebral and Cardiovascular Centre published in Heart Journal.
