Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation

Say, Irene; Chen, Yiling Elaine; Sun, Matthew Z.; Li, Jingyi Jessica; Lu, Daniel C.

doi:10.3389/fresc.2022.1005168

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…While the IMPACT model is well-established and focuses on clinical, imaging, and demographic factors, our study aims to supplement it by incorporating blood biomarkers and other additional predictors. Furthermore, there are studies describing models to predict the functional outcome or The Glasgow Outcome Scale-Extended, 5,[25][26][27][28][29] and more recent studies using images as inputs. 8,30,31 Furthermore, there are studies in the literature similar to ours that describe models for predicting in-hospital mortality, 29,[32][33][34][35] early mortality, [36][37][38] discharge position, 39,40 need for hospital admission, 6 emergency neurosurgery, 41 and length of hospital stay.…”

Section: Discussionmentioning

confidence: 99%

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study

Zhu,

Ozkara,

Chen

et al. 2023

Neuroradiol J

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Purpose We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients. Methods In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables. Results Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3’s hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis. Conclusion Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.

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Section: Discussionmentioning

confidence: 99%

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study

Zhu,

Ozkara,

Chen

et al. 2023

Neuroradiol J

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“…Compared with LM, advanced ML methods allow greater flexibility for modeling non-linear recovery pattern, interactions between treatments, diminishing returns, ceiling/floor effect, which better reflects real-world settings. 6 A few studies have applied ML methods to rehabilitation data and predict outcomes in different patient populations affected by mild TBI, 7 , 8 stroke, 9 , 10 and predict FIM scores at discharge, 11 survival or mortality probability after TBI, 6 , 12 , 13 , 14 , 15 , 16 , 17 , 18 suicidal ideation after TBI. 19 In contrast, Bruschetta et al 20 did not find ML methods to have superiority over LM in predicting outcome after TBI and was limited by quantity of predictor variables.…”

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confidence: 99%

“… 19 In contrast, Bruschetta et al 20 did not find ML methods to have superiority over LM in predicting outcome after TBI and was limited by quantity of predictor variables. 11 , 20 …”

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confidence: 99%

Traumatic Brain Injury Rehabilitation Outcome Prediction Using Machine Learning Methods

Appiah Balaji,

Beaulieu,

Bogner

et al. 2023

Archives of Rehabilitation Research and Clinical Translation

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Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department

Mekkodathil,

El-Menyar,

Naduvilekandy

et al. 2023

Diagnostics

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Background: Accurate prediction of in-hospital mortality is essential for better management of patients with traumatic brain injury (TBI). Machine learning (ML) algorithms have been shown to be effective in predicting clinical outcomes. This study aimed to identify predictors of in-hospital mortality in TBI patients using ML algorithms. Materials and Method: A retrospective study was performed using data from both the trauma registry and electronic medical records among TBI patients admitted to the Hamad Trauma Center in Qatar between June 2016 and May 2021. Thirteen features were selected for four ML models including a Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XgBoost), to predict the in-hospital mortality. Results: A dataset of 922 patients was analyzed, of which 78% survived and 22% died. The AUC scores for SVM, LR, XgBoost, and RF models were 0.86, 0.84, 0.85, and 0.86, respectively. XgBoost and RF had good AUC scores but exhibited significant differences in log loss between the training and testing sets (% difference in logloss of 79.5 and 41.8, respectively), indicating overfitting compared to the other models. The feature importance trend across all models indicates that aPTT, INR, ISS, prothrombin time, and lactic acid are the most important features in prediction. Magnesium also displayed significant importance in the prediction of mortality among serum electrolytes. Conclusions: SVM was found to be the best-performing ML model in predicting the mortality of TBI patients. It had the highest AUC score and did not show overfitting, making it a more reliable model compared to LR, XgBoost, and RF.

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Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation

Cited by 3 publications

References 41 publications

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study

Traumatic Brain Injury Rehabilitation Outcome Prediction Using Machine Learning Methods

Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department

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