Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records
BackgroundTraumatic Brain Injury (TBI) is one of the leading causes of injury related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences, complicating medical interpretation and prognosis. Gathering clinical, demog...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2022.859068/full |
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| author | João Fonseca Xiuyun Liu Hélder P. Oliveira Hélder P. Oliveira Tania Pereira |
| author_facet | João Fonseca Xiuyun Liu Hélder P. Oliveira Hélder P. Oliveira Tania Pereira |
| author_sort | João Fonseca |
| collection | DOAJ |
| description | BackgroundTraumatic Brain Injury (TBI) is one of the leading causes of injury related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences, complicating medical interpretation and prognosis. Gathering clinical, demographic, and laboratory data to perform a prognosis requires time and skill in several clinical specialties. Machine learning (ML) methods can take advantage of the data and guide physicians toward a better prognosis and, consequently, better healthcare. The objective of this study was to develop and test a wide range of machine learning models and evaluate their capability of predicting mortality of TBI, at hospital discharge, while assessing the similarity between the predictive value of the data and clinical significance.MethodsThe used dataset is the Hackathon Pediatric Traumatic Brain Injury (HPTBI) dataset, composed of electronic health records containing clinical annotations and demographic data of 300 patients. Four different classification models were tested, either with or without feature selection. For each combination of the classification model and feature selection method, the area under the receiver operator curve (ROC-AUC), balanced accuracy, precision, and recall were calculated.ResultsMethods based on decision trees perform better when using all features (Random Forest, AUC = 0.86 and XGBoost, AUC = 0.91) but other models require prior feature selection to obtain the best results (k-Nearest Neighbors, AUC = 0.90 and Artificial Neural Networks, AUC = 0.84). Additionally, Random Forest and XGBoost allow assessing the feature's importance, which could give insights for future strategies on the clinical routine.ConclusionPredictive capability depends greatly on the combination of model and feature selection methods used but, overall, ML models showed a very good performance in mortality prediction for TBI. The feature importance results indicate that predictive value is not directly related to clinical significance. |
| first_indexed | 2024-04-12T17:49:05Z |
| format | Article |
| id | doaj.art-4bd36d8eeed441c68754e164c16ed649 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-12T17:49:05Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-4bd36d8eeed441c68754e164c16ed6492022-12-22T03:22:33ZengFrontiers Media S.A.Frontiers in Neurology1664-22952022-06-011310.3389/fneur.2022.859068859068Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health RecordsJoão Fonseca0Xiuyun Liu1Hélder P. Oliveira2Hélder P. Oliveira3Tania Pereira4Institute for Systems and Computer Engineering, Technology and Science, Porto, PortugalDepartment of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, United StatesInstitute for Systems and Computer Engineering, Technology and Science, Porto, PortugalFaculty of Science, University of Porto, Porto, PortugalInstitute for Systems and Computer Engineering, Technology and Science, Porto, PortugalBackgroundTraumatic Brain Injury (TBI) is one of the leading causes of injury related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences, complicating medical interpretation and prognosis. Gathering clinical, demographic, and laboratory data to perform a prognosis requires time and skill in several clinical specialties. Machine learning (ML) methods can take advantage of the data and guide physicians toward a better prognosis and, consequently, better healthcare. The objective of this study was to develop and test a wide range of machine learning models and evaluate their capability of predicting mortality of TBI, at hospital discharge, while assessing the similarity between the predictive value of the data and clinical significance.MethodsThe used dataset is the Hackathon Pediatric Traumatic Brain Injury (HPTBI) dataset, composed of electronic health records containing clinical annotations and demographic data of 300 patients. Four different classification models were tested, either with or without feature selection. For each combination of the classification model and feature selection method, the area under the receiver operator curve (ROC-AUC), balanced accuracy, precision, and recall were calculated.ResultsMethods based on decision trees perform better when using all features (Random Forest, AUC = 0.86 and XGBoost, AUC = 0.91) but other models require prior feature selection to obtain the best results (k-Nearest Neighbors, AUC = 0.90 and Artificial Neural Networks, AUC = 0.84). Additionally, Random Forest and XGBoost allow assessing the feature's importance, which could give insights for future strategies on the clinical routine.ConclusionPredictive capability depends greatly on the combination of model and feature selection methods used but, overall, ML models showed a very good performance in mortality prediction for TBI. The feature importance results indicate that predictive value is not directly related to clinical significance.https://www.frontiersin.org/articles/10.3389/fneur.2022.859068/fullmachine learningfeature selectionfeature importanceTraumatic Brain Injurymortality predictionclinical significance |
| spellingShingle | João Fonseca Xiuyun Liu Hélder P. Oliveira Hélder P. Oliveira Tania Pereira Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records Frontiers in Neurology machine learning feature selection feature importance Traumatic Brain Injury mortality prediction clinical significance |
| title | Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records |
| title_full | Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records |
| title_fullStr | Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records |
| title_full_unstemmed | Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records |
| title_short | Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records |
| title_sort | learning models for traumatic brain injury mortality prediction on pediatric electronic health records |
| topic | machine learning feature selection feature importance Traumatic Brain Injury mortality prediction clinical significance |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2022.859068/full |
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