In a new study from the University of Michigan and elsewhere, researchers found that a branch of artificial intelligence (AI), called machine learning, can accurately predict the risk of an out of hospital cardiac arrest—when the heart suddenly stops beating—using a combination of timing and weather data.
They found the risk of a cardiac arrest was highest on Sundays, Mondays, public holidays and when temperatures dropped sharply within or between days.
This information could be used as an early warning system for citizens, to lower their risk and improve their chances of survival, and to improve the preparedness of emergency medical services.
Out of hospital cardiac arrest is common around the world, but is generally associated with low rates of survival. Risk is affected by prevailing weather conditions.
But meteorological data are extensive and complex, and machine learning has the potential to pick up associations not identified by conventional one-dimensional statistical approaches.
Machine learning is the study of computer algorithms, and based on the idea that systems can learn from data and identify patterns to inform decisions with minimal intervention.
In the study, the team assessed the capacity of machine learning to predict daily out-of-hospital cardiac arrest, using daily weather and timing data.
Of 1,299,784 cases occurring between 2005 and 2013, machine learning was applied to 525,374, using either weather or timing data, or both (training dataset).
The results were then compared with 135,678 cases occurring in 2014-15 to test the accuracy of the model for predicting the number of daily cardiac arrests in other years (testing dataset).
And to see how accurate the approach might be at the local level, the researchers carried out a ‘heatmap analysis,’ using another dataset drawn from the location of out-of-hospital cardiac arrests in Kobe city between January 2016 and December 2018.
They found the combination of weather and timing data most accurately predicted an out-of-hospital cardiac arrest in both the training and testing datasets.
It predicted that Sundays, Mondays, public holidays, winter, low temperatures and sharp temperature drops within and between days were more strongly associated with cardiac arrest than either the weather or timing data alone.
The researchers acknowledge that they didn’t have detailed information on the location of cardiac arrests except in Kobe city, nor did they have any data on pre-existing medical conditions, both of which may have influenced the results.
But the predictive model for daily incidence of out-of-hospital cardiac arrest is widely generalizable for the general population in developed countries because this study had a large sample size and used comprehensive meteorological data.
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The study is published in the journal Heart. One author of the study is Takahiro Nakashima.
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