A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months

Abstract

Background: Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcomes in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings.Methods: In this study, we examined the capability of a machine learning-based model in predicting “favorable” or “unfavorable” outcomes after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI patients admitted to the neuro-intensive care unit of Rajaee (Emtiaz) Hospital (Shiraz, Iran) between 2015 and 2017. Model performance was evaluated using three indices: sensitivity, specificity, and accuracy. A ten-fold cross-validation method was used to estimate these indices.Results: Overall, the developed models showed excellent performance with AUC around 0.81, sensitivity and specificity of around 0.78. The top-three factors important in predicting 6-month post-trauma survival status in TBI patients are “Glasgow coma scale motor response,” “pupillary reactivity,” and “age.”Conclusions: Machine learning techniques might be used to predict the 6-month outcome in TBI patients using only the parameters measured on admission when the machine learning is trained using a large data set.

Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics

“…All statistical methods showed the same performance in predicting mortality or unfavorable outcomes (ranging from 79% to 82%), where the RF algorithm was the worst. Similarly, Nourelahi et al [ 31 ] described the same results by evaluating 2381 TBI patients. Despite the employment of the only SVM and RF for ML analysis, they reached an accuracy in post-trauma survival status prediction of 79%, where the best features extracted were Glasgow coma scale motor response, pupillary reactivity and age.…”

“…In TBI patients a similar status is reported, with accuracy ranging from 78% to 98% and no evidence of the best ML algorithm. Indeed, either considering works using mixed or ensemble ML models [ 30 , 31 , 33 , 34 ] or that with one single algorithm [ 32 , 35 ], the result is similar: no evidence for a best ML algorithm and no substantial difference with respect to LR approach.…”

Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care

“…This is especially important post-traumatic brain injury (TBI) where long-term effects can be unpredictable. Pang et al [ 47 ] and Nourelahi et al [ 46 ] compared the efficacy of several algorithm types to predict Glasgow Outcome Scoring for patients’ post-TBI. Both used demographics, GCS, and pupillary responses (with some additional unique inputs per study) and were able to achieve accuracy of 63–78% [ 46 , 47 ].…”

“…Pang et al [ 47 ] and Nourelahi et al [ 46 ] compared the efficacy of several algorithm types to predict Glasgow Outcome Scoring for patients’ post-TBI. Both used demographics, GCS, and pupillary responses (with some additional unique inputs per study) and were able to achieve accuracy of 63–78% [ 46 , 47 ]. Compared to other clinical applications of AI in trauma discussed in this paper, TBI outcome appears to be of lower accuracy, likely due to the high variability in patient recovery post-injury.…”

Robust Ensemble Morph Detection with Domain Generalization

“…Machine learning paradigms [37,35,4,38,34] are built upon the assumption that training and test data have the same probability distributions. When this hypothesis is even marginally broken, as most real-life settings, a significant drop in performance can be observed.…”