2016
DOI: 10.1371/journal.pone.0158762
|View full text |Cite
|
Sign up to set email alerts
|

Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis

Abstract: BackgroundTraumatic brain injury remains a global health problem. Understanding the relative importance of outcome predictors helps optimize our treatment strategies by informing assessment protocols, clinical decisions and trial designs. In this study we establish importance ranking for outcome predictors based on receiver operating indices to identify key predictors of outcome and create simple predictive models. We then explore the associations between key outcome predictors using Bayesian networks to gain … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
33
0
1

Year Published

2017
2017
2020
2020

Publication Types

Select...
4
2
2
1

Relationship

2
7

Authors

Journals

citations
Cited by 41 publications
(34 citation statements)
references
References 36 publications
0
33
0
1
Order By: Relevance
“…We also tested internal validation on the trauma dataset using k-fold cross-validation. Model performance was assessed using area under the receiver operator curve (33,34).…”
Section: Methodsmentioning
confidence: 99%
“…We also tested internal validation on the trauma dataset using k-fold cross-validation. Model performance was assessed using area under the receiver operator curve (33,34).…”
Section: Methodsmentioning
confidence: 99%
“…We analyzed the dataset of Corticosteroid Randomization after Significant Head Injury (CRASH) trial [47] using an implementation of ESPRESSO-II in R [48]. Study variables include demographics, injury characteristics and computed tomography (CT) findings which are clinically important predictors of TBI outcome [48] [49] [50]. Outcome measure is death or severe disability versus moderate disability or good recovery at 6 months.…”
Section: Analyzing Observational Datamentioning
confidence: 99%
“…Outcome measure is death or severe disability versus moderate disability or good recovery at 6 months. For a full description of the dataset we refer the readers to [50]. Table 5.…”
Section: Analyzing Observational Datamentioning
confidence: 99%
“…Critical care is one of the most data-intense health care environments (17) yielding a broad range of high dimensional data (18).This lends itself well to advanced analytics such as machine learning (19,20), which allows detection of complex, clinically relevant patterns. Latent class analysis has become increasingly utilized in the discovery of clinically relevant patient subclasses (14,15,21).…”
Section: Introductionmentioning
confidence: 99%