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Diagnostics
Special Issues
Recent Advances in Diagnosis and Treatment in Trauma and Emergency...
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Recent Advances in Diagnosis and Treatment in Trauma and Emergency Medicine

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1922

Special Issue Editor

Dr. Ayman El-Menyar

E-Mail Website
Guest Editor
1. Director of Clinical Research, Trauma & Vascular Surgery, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar
2. Department of Clinical Medicine, Weill Cornell Medical College, Doha P.O. Box 24144, Qatar
Interests: heart failure; atherosclerosis; diabetes; myocardial infarction; echocardiography; hypertension; critical care medicine; injury prevention; cardiopulmonary resuscitation; blood pressure

Special Issue Information

Dear Colleagues, 

The aim is to publish high-quality papers on the up-to-date diagnostic and therapeutic modalities in trauma and emergency medicine. This will also address the guidelines and recent stepwise approach as well as the multidisciplinary approach in traumatic injury and acute medical and/or surgical settings. Personalized medicine and genetic background in emergency conditions also fall in the scope of this issue. The role of artificial intelligence/ machine learning in acute medical and surgical conditions will be addressed. Bioclinical markers in trauma and emergency situations are also hot topics for research.

Dr. Ayman El-Menyar
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

15 pages, 2217 KiB  
Article
Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department
by Ahammed Mekkodathil, Ayman El-Menyar, Mashhood Naduvilekandy, Sandro Rizoli and Hassan Al-Thani
Diagnostics 2023, 13(15), 2605; https://doi.org/10.3390/diagnostics13152605 - 5 Aug 2023
Cited by 1 | Viewed by 1587
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Recent Advances in Diagnosis and Treatment in Trauma and Emergency Medicine)
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