An algorithm based on deep learning for predicting in-hospital cardiac arrest

Journal of the American Heart Association

Volumn 7, Issue 13, 2018, Pages

  Kwon, Joon Myoung ^a   Lee, Youngnam ^b   Lee, Yeha ^b   Lee, Seungwoo ^b   Park, Jinsik ^a  

^a Mediplex Sejong Hospital   (South Korea)

^b VUNO Inc   (South Korea)

Author keywords

Artificial intelligence; Cardiac arrest; Deep learning; Machine learning; Rapid response system; Resuscitation

Indexed keywords

ADULT; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BLOOD PRESSURE; COHORT ANALYSIS; CONTROLLED STUDY; DEEP LEARNING BASED EARLY WARNING SYSTEM; FEMALE; HEART ARREST; HEART RATE; HOSPITAL ADMISSION; HOSPITAL PATIENT; HUMAN; LEARNING; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; PREDICTION; PRIORITY JOURNAL; RANDOM FOREST; RAPID RESPONSE TEAM; RECEIVER OPERATING CHARACTERISTIC; RESUSCITATION; RETROSPECTIVE STUDY; SENSITIVITY ANALYSIS; TRACK AND TRIGGER SYSTEM; VITAL SIGN; AGED; CLINICAL TRIAL; COMPUTER ASSISTED DIAGNOSIS; DECISION SUPPORT SYSTEM; EARLY DIAGNOSIS; MIDDLE AGED; MORTALITY; MULTICENTER STUDY; PREDICTIVE VALUE; PROGNOSIS; REPRODUCIBILITY; RISK ASSESSMENT; RISK FACTOR; SOUTH KOREA; TIME FACTOR;

ADULT; AGED; DECISION SUPPORT TECHNIQUES; DEEP LEARNING; DIAGNOSIS, COMPUTER-ASSISTED; EARLY DIAGNOSIS; FEMALE; HEART ARREST; HUMANS; INPATIENTS; MALE; MIDDLE AGED; PREDICTIVE VALUE OF TESTS; PROGNOSIS; REPRODUCIBILITY OF RESULTS; RESUSCITATION; RETROSPECTIVE STUDIES; RISK ASSESSMENT; RISK FACTORS; SEOUL; TIME FACTORS; VITAL SIGNS;

EID: 85049690743     PISSN: None     EISSN: 20479980     Source Type: Journal    
DOI: 10.1161/JAHA.118.008678     Document Type: Article

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