A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit

Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs. Early detection and appropriate treatment of BSI may improve patient's outcome. Data on machine-learning models to predict BSI in pediatric pat...

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Veröffentlicht in:	PloS one 2024-05, Vol.19 (5), p.e0299884-e0299884
Hauptverfasser:	Liporaci, Felipe, Carlotti, Danilo, Carlotti, Ana
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Adolescent Algorithms Analysis Artificial intelligence Bacteremia Bacteremia - diagnosis Biology and Life Sciences Blood Blood cells Blood culture Blood pressure Body mass index C-reactive protein Care and treatment Catheters Cell culture Child Child, Preschool Children Computer and Information Sciences Contaminants Data collection Datasets Decision making Demographics Demography Diagnosis Early Diagnosis Electronic health records Electronic medical records Engineering and Technology Female Health aspects Health care reform Heart beat Heart rate Hospital costs Humans Infant Infant, Newborn Infection Infections Intensive care Intensive Care Units, Pediatric Laboratories Learning algorithms Machine Learning Male Medical care, Cost of Medical diagnosis Medical examination Medical records Medical research Medicine and Health Sciences Medicine, Experimental Morbidity Mortality Nutrition Oxygen content Parenteral nutrition Patients Pediatric intensive care Pediatrics Performance evaluation Prognosis Respiration Respiratory rate Retrospective Studies Sensitivity Sepsis - blood Sepsis - diagnosis Time series Variables Vital signs
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