Predicting Recovery in Patients Suffering from Traumatic Brain Injury By Using Admission Variables and Physiological Data: A Comparison between Decision Tree Analysis and Logistic Regression
Authors: Peter J.D. Andrews, M.D., F.R.C.A., Derek H. Sleeman, Ph.D., F.R.S. (ED), Patrick F.X. Statham F.R.C.S (Sn), Andrew McQuatt M. Sc., Vincent Corruble Ph.D., Patricia A. Jones, M. App. Sci., Timothy P. Howells, Ph.D., and Carol S. A. Macmillan, M.R.C.P., F.R.C.A
This study compared results given by logistic regression and decision tree analysis on patients suffering from Traumatic Brain Injury. The researchers studied 124 patients in an intensive care unit over a 12 month period by using a data collection system. They input the values into a logistic regression, then created a decision tree from root nodes to target classes. This is called the Glasgow Outcome Score.
The researchers then assessed the outcome after 12 months of 69 patients with 8 insult or head injury categories. They input the data into a logistic regression to assess how patient age, Glasgow Coma Scale Score, Injury Severity Score, pupilary response upon admission, and insult duration. After they input the data, they found hypotensive, pyrexic, and hypoxemic insults to be the most significant predictors of mortality.
After they used decision tree analysis, they found that hypotension and low cerebral perfusion pressure to be the most significant predictors of death. The analysis proved to 9.2 percent more accurate than just using the largest outcome category as a predictor of mortality.
The article gave a good explanation for why decision tree makes for more accurate analysis. It also did a decent job of listing the variables used for the analysis. However, this is one study done on the decision tree analysis in the medical field and more specifically dealing with brain injuries. The study will need to be replicated using different numbers of patients to give an complete assessment of how accurate decision tree analysis is in that field.