Machine learning helps predict survival rates from out-of-hospital cardiac arrest



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DALLAS, November 9, 2019 – Using local and neighborhood data in combination with existing information sources creates a more accurate prediction about a patient’s prospects for recovery after out-of-hospital cardiac arrest (OHCA), according to preliminary research to be presented at the American Heart Association’s Resuscitation Science Symposium 2020. The 2020 meeting will be held virtually from November 14-16 and will present the latest advances in the treatment of cardiopulmonary arrest and life-threatening traumatic injuries.

Machine learning algorithms were developed and tested on nearly 10,000 OHCA cases that occurred in Chicago’s 77 wards between 2014 and 2019. The researchers used OHCA information from the existing cardiac arrest database to improve survival ( CARES) to identify incidents that occurred outside of a health care system or nursing home in the Chicago area. Information on individual Chicago Health Atlas (CHA) communities was then added, including crime rates, access to health care and education.

Researchers combined CARES and CHA information to train a machine learning model to predict OHCA survival. Adding the CHA data increased the mean recall of OHCA survival predictions from 84.5 to nearly 87%.

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“This is exciting,” says the study’s lead author, Samuel Harford, MS, Ph.D. candidate in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago. “We were able to provide a machine learning model with information from publicly available real-world sources that helped us find models that would otherwise have been invisible, therefore, achieving better results. This strategy has the potential to be useful in forecasting. more accurate than other clinical results in future studies. ”

The study had limitations based on the quality of the data and further information that could impact the results such as weather, traffic, EMS routes and socioeconomic status has yet to be examined.

Co-authors are Houshang Darabi, Ph.D .; Sara Heinert, Ph.D., MPH; Marina Del Rios, MD, MS The author’s revelations are abstract.

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