Seeing the forest beyond the trees: Predicting survival in burn patients with machine learning. Am J Surg 2018 Mar;215(3):411-416
Date
11/12/2017Pubmed ID
29126594Pubmed Central ID
PMC5837911DOI
10.1016/j.amjsurg.2017.10.027Scopus ID
2-s2.0-85032977530 (requires institutional sign-in at Scopus site) 17 CitationsAbstract
BACKGROUND: This study aims to identify predictors of survival for burn patients at the patient and hospital level using machine learning techniques.
METHODS: 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 Survey. Random forest and stochastic gradient boosting (SGB) were used to identify predictors of survival at the patient and hospital level from the top performing model.
RESULTS: We analyzed 31,350 patients from 670 hospitals. SGB (AUC 0.93) and random forest (AUC 0.82) best identified patient factors such as age and absence of renal failure (p < 0.001) and hospital factors such as full time residents (p < 0.001) and nurses (p = 0.004) to be associated with increased survival.
CONCLUSIONS: Patient and hospital factors are predictive of survival in burn patients. It is difficult to control patient factors, but hospital factors can inform decisions about where burn patients should be treated.
Author List
Cobb AN, Daungjaiboon W, Brownlee SA, Baldea AJ, Sanford AP, Mosier MM, Kuo PCAuthor
Adrienne Cobb MD Assistant Professor in the Surgery department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AdolescentAdult
Aged
Aged, 80 and over
Burns
Child
Child, Preschool
Clinical Decision-Making
Decision Support Techniques
Decision Trees
Female
Hospitals
Humans
Infant
Infant, Newborn
Machine Learning
Male
Middle Aged
Prognosis
United States
Young Adult
