Seeing the forest beyond the trees: Predicting survival in burn patients with machine learning

This study aims to identify predictors of survival for burn patients at the patient and hospital level using machine learning techniques. The HCUP SID for California, Florida and New York were used to identify patients admitted with a burn diagnosis and merged with hospital data from the AHA Annual...

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Veröffentlicht in:	The American journal of surgery 2018-03, Vol.215 (3), p.411-416
Hauptverfasser:	Cobb, Adrienne N., Daungjaiboon, Witawat, Brownlee, Sarah A., Baldea, Anthony J., Sanford, Arthur P., Mosier, Michael M., Kuo, Paul C.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adolescent Adult Age Aged Aged, 80 and over Artificial intelligence Burn patients Burns Burns - diagnosis Burns - mortality Cancer Child Child, Preschool Clinical Decision-Making Clinical outcomes Decision Support Techniques Decision Trees Female Hospitals Humans Infant Infant, Newborn Injuries Learning algorithms Machine Learning Male Medical personnel Medical prognosis Middle Aged Mortality Outcomes Patients Predictive control Prognosis Random forest Renal failure Stochasticity Surgery Surgical outcomes Survival Trauma United States - epidemiology Variables Workforce planning Young Adult
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