Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department
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 mortalit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/15/2605 |
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| author | Ahammed Mekkodathil Ayman El-Menyar Mashhood Naduvilekandy Sandro Rizoli Hassan Al-Thani |
| author_facet | Ahammed Mekkodathil Ayman El-Menyar Mashhood Naduvilekandy Sandro Rizoli Hassan Al-Thani |
| author_sort | Ahammed Mekkodathil |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:29:47Z |
| format | Article |
| id | doaj.art-cf0a9b9f0e2b444fb67c32dd6eceed18 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T00:29:47Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-cf0a9b9f0e2b444fb67c32dd6eceed182023-11-18T22:47:44ZengMDPI AGDiagnostics2075-44182023-08-011315260510.3390/diagnostics13152605Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency DepartmentAhammed Mekkodathil0Ayman El-Menyar1Mashhood Naduvilekandy2Sandro Rizoli3Hassan Al-Thani4Clinical Research, Trauma and Vascular Surgery, Hamad Medical Corporation, Doha P.O. Box 3050, QatarClinical Research, Trauma and Vascular Surgery, Hamad Medical Corporation, Doha P.O. Box 3050, QatarData Science, Alpineaid Management, Ernakulam 682304, IndiaTrauma Surgery Section, Hamad General Hospital (HGH), Doha P.O. Box 3050, QatarTrauma Surgery Section, Hamad General Hospital (HGH), Doha P.O. Box 3050, QatarBackground: 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.https://www.mdpi.com/2075-4418/13/15/2605traumabrain injuryheadmachine learningpredictorssupport vector machine |
| spellingShingle | Ahammed Mekkodathil Ayman El-Menyar Mashhood Naduvilekandy Sandro Rizoli Hassan Al-Thani Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department Diagnostics trauma brain injury head machine learning predictors support vector machine |
| title | Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department |
| title_full | Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department |
| title_fullStr | Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department |
| title_full_unstemmed | Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department |
| title_short | Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department |
| title_sort | machine learning approach for the prediction of in hospital mortality in traumatic brain injury using bio clinical markers at presentation to the emergency department |
| topic | trauma brain injury head machine learning predictors support vector machine |
| url | https://www.mdpi.com/2075-4418/13/15/2605 |
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