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T1  - Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality : A retrospective study 
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UR  - http://hdl.handle.net/10138/334541 
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A1  - Pirneskoski, Jussi; Tamminen, Joonas; Kallonen, Antti; Nurmi, Jouni; Kuisma, Markku; Olkkola, Klaus T.; Hoppu, Sanna 
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Y1  - 2020 
LA  - eng 
AB  - Aim of the study: The National Early Warning Score (NEWS) is a validated method for predicting clinical deterioration in hospital wards, but its performance in prehospital settings remains controversial. Modern machine learning models may outperform traditional statistical analyses for predicting short-term mortality. Thus, we aimed to compare the mortality prediction accuracy of NEWS and random forest machine learning using prehospital vital signs. Methods: In this retrospective study, all elec... 
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KW  - Emergency medical services; Prehospital; Cardiac arrest prevention; Early warning score; National Early Warning Score; NEWS; Random forest; Machine learning; IN-HOSPITAL MORTALITY; CARE-UNIT ADMISSION; RISK; 3126 Surgery, anesthesiology, intensive care, radiology 
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