Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach
<h3>Background</h3><p dir="ltr">The use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that the machine learning predictive techniques outperform the classical multivariate techniques. We aimed...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , |
| Published: |
2022
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513566573330432 |
|---|---|
| author | Ahmad Abujaber (9100064) |
| author2 | Adam Fadlalla (9100067) Diala Gammoh (9100070) Husham Abdelrahman (768893) Monira Mollazehi (9100073) Ayman El-Menyar (440103) |
| author2_role | author author author author author |
| author_facet | Ahmad Abujaber (9100064) Adam Fadlalla (9100067) Diala Gammoh (9100070) Husham Abdelrahman (768893) Monira Mollazehi (9100073) Ayman El-Menyar (440103) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmad Abujaber (9100064) Adam Fadlalla (9100067) Diala Gammoh (9100070) Husham Abdelrahman (768893) Monira Mollazehi (9100073) Ayman El-Menyar (440103) |
| dc.date.none.fl_str_mv | 2022-11-22T21:18:14Z |
| dc.identifier.none.fl_str_mv | 10.1186/s13049-020-00738-5 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Prediction_of_in-hospital_mortality_in_patients_with_post_traumatic_brain_injury_using_National_Trauma_Registry_and_Machine_Learning_Approach/21598473 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Prediction models Traumatic brain injury Machine learning approach |
| dc.title.none.fl_str_mv | Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">The use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that the machine learning predictive techniques outperform the classical multivariate techniques. We aimed to build a machine learning predictive model to predict the in-hospital mortality for patients who sustained Traumatic Brain Injury (TBI).</p><h3>Methods</h3><p dir="ltr">Adult patients with TBI who were hospitalized in the level 1 trauma center in the period from January 2014 to February 2019 were included in this study. Patients’ demographics, injury characteristics and CT findings were used as predictors. The predictive performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) was evaluated in terms of accuracy, Area Under the Curve (AUC), sensitivity, precision, Negative Predictive Value (NPV), specificity and F-score.</p><h3>Results</h3><p dir="ltr">A total of 1620 eligible patients were included in the study (1417 survival and 203 non-survivals). Both models achieved accuracy over 91% and AUC over 93%. SVM achieved the optimal performance with accuracy 95.6% and AUC 96%.</p><h3>Conclusions</h3><p dir="ltr">for prediction of mortality in patients with TBI, SVM outperformed the well-known classical models that utilized the conventional multivariate analytical techniques.</p><h2>Other Information</h2><p dir="ltr">Published in: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1186/s13049-020-00738-5" target="_blank">http://dx.doi.org/10.1186/s13049-020-00738-5</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_dda611ede21c617e3f8b8b07c17fc381 |
| identifier_str_mv | 10.1186/s13049-020-00738-5 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21598473 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning ApproachAhmad Abujaber (9100064)Adam Fadlalla (9100067)Diala Gammoh (9100070)Husham Abdelrahman (768893)Monira Mollazehi (9100073)Ayman El-Menyar (440103)Biomedical and clinical sciencesClinical sciencesEngineeringBiomedical engineeringInformation and computing sciencesArtificial intelligencePrediction modelsTraumatic brain injuryMachine learning approach<h3>Background</h3><p dir="ltr">The use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that the machine learning predictive techniques outperform the classical multivariate techniques. We aimed to build a machine learning predictive model to predict the in-hospital mortality for patients who sustained Traumatic Brain Injury (TBI).</p><h3>Methods</h3><p dir="ltr">Adult patients with TBI who were hospitalized in the level 1 trauma center in the period from January 2014 to February 2019 were included in this study. Patients’ demographics, injury characteristics and CT findings were used as predictors. The predictive performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) was evaluated in terms of accuracy, Area Under the Curve (AUC), sensitivity, precision, Negative Predictive Value (NPV), specificity and F-score.</p><h3>Results</h3><p dir="ltr">A total of 1620 eligible patients were included in the study (1417 survival and 203 non-survivals). Both models achieved accuracy over 91% and AUC over 93%. SVM achieved the optimal performance with accuracy 95.6% and AUC 96%.</p><h3>Conclusions</h3><p dir="ltr">for prediction of mortality in patients with TBI, SVM outperformed the well-known classical models that utilized the conventional multivariate analytical techniques.</p><h2>Other Information</h2><p dir="ltr">Published in: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1186/s13049-020-00738-5" target="_blank">http://dx.doi.org/10.1186/s13049-020-00738-5</a></p>2022-11-22T21:18:14ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s13049-020-00738-5https://figshare.com/articles/journal_contribution/Prediction_of_in-hospital_mortality_in_patients_with_post_traumatic_brain_injury_using_National_Trauma_Registry_and_Machine_Learning_Approach/21598473CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215984732022-11-22T21:18:14Z |
| spellingShingle | Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach Ahmad Abujaber (9100064) Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Prediction models Traumatic brain injury Machine learning approach |
| status_str | publishedVersion |
| title | Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach |
| title_full | Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach |
| title_fullStr | Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach |
| title_full_unstemmed | Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach |
| title_short | Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach |
| title_sort | Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach |
| topic | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Prediction models Traumatic brain injury Machine learning approach |