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, finds research published online in the journal Heart. 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. The risk of a cardiac arrest was highest on Sundays, Mondays, public holidays and when temperatures dropped sharply within or between days, the findings show. 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, suggest the researchers. Out of hospital cardiac arrest is common around the world, but is generally associated with low rates of survival.

In‐hospital cardiac arrest is a major burden to public health, which affects patient safety.1, Two types of TTS are used in RRSs. For the single‐parameter TTS (SPTTS), cardiac arrest is predicted if any single vital sign (eg, heart rate [HR], blood pressure) is out of the normal range.14 The aggregated weighted TTS calculates a weighted score for each vital sign and then finds patients with cardiac arrest based on the sum of these scores.15 The modified early warning score (MEWS) is one of the most widely used approaches among all aggregated weighted TTSs (Table 1)16; however, traditional TTSs including MEWS have limitations, with low sensitivity or high false‐alarm rates.14, 15, 17 Sensitivity and false‐alarm rate interact: Increased sensitivity creates higher false‐alarm rates and vice versa.

Corti, the Copenhagen-based company, is to enter into a partnership with the European Emergency Number Association (EENA). Under the initiative four sites across Europe have been selected to pilot the technology. The project could change the way emergency medical calls are handled in the future. Currently Corti is being deployed by Copenhagen Emergency Medical Services in order to detect cardiac arrests during emergency calls. Data suggests that Corti is 20 percent more accurate at detecting Out of Hospital Cardiac Arrests than medical dispatchers.