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. 2018 Mar;215(3):411-416.
doi: 10.1016/j.amjsurg.2017.10.027. Epub 2017 Nov 7.

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

Adrienne N Cobb  1 , Witawat Daungjaiboon  2 , Sarah A Brownlee  3 , Anthony J Baldea  4 , Arthur P Sanford  5 , Michael M Mosier  6 , Paul C Kuo  7
Affiliations

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

Adrienne N Cobb et al. Am J Surg. 2018 Mar.

Abstract

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.

Keywords: Burns; Machine learning; Outcomes; Random forest; Survival.

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Conflict of interest statement

The authors have no conflicts of interest to disclose

Figures

Figure I
Figure I
Violin plot with age distribution and mortality by age ranges with LD50. The shape of the violin plot shows each age range distribution. LD50=39 indicating patients at age more than 39 have 50% change of death. *Figure to be used in color in online version and grayscale in print.
Figure II
Figure II
Mortality rates by age ranges and burn type. As age increases mortality from burn injury increases. Moralities per burn type are listed vertically. *Figure to be used in color in online version and grayscale in print.
Figure III
Figure III
Variable importance for (A) stochastic gradient boosting and (B) random forest algorithms at the patient level. Variable importance is without units, but is normalized to a scale of 1 to 100, with the larger number indicating increased importance. *Figure to be used in color in online version and grayscale in print.
Figure IV
Figure IV
Random forest variable importance at the hospital level. Variable importance is without units, but is normalized to a scale of 1 to 100, with the larger number indicating increased importance. *Figure to be used in color in online version and grayscale in print.
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