Prognosis prediction in traumatic brain injury patients using machine learning algorithms
Article
Khalili, Hosseinali, Rismani, Maziyar, Nematollahi, Mohammad Ali, Masoudi, Mohammad Sadegh, Asadollahi, Arefeh, Taheri, Reza, Pourmontaseri, Hossein, Valibeygi, Adib, Roshanzamir, Mohamad, Alizadehsani, Roohallah, Niakan, Amin, Andishgar, Aref, Islam, Sheikh Mohammed Shariful and Acharya, U. Rajendra. 2023. "Prognosis prediction in traumatic brain injury patients using machine learning algorithms." Scientific Reports. 13 (1). https://doi.org/10.1038/s41598-023-28188-w
| Article Title | Prognosis prediction in traumatic brain injury patients using machine learning algorithms |
|---|---|
| ERA Journal ID | 201487 |
| Article Category | Article |
| Authors | Khalili, Hosseinali, Rismani, Maziyar, Nematollahi, Mohammad Ali, Masoudi, Mohammad Sadegh, Asadollahi, Arefeh, Taheri, Reza, Pourmontaseri, Hossein, Valibeygi, Adib, Roshanzamir, Mohamad, Alizadehsani, Roohallah, Niakan, Amin, Andishgar, Aref, Islam, Sheikh Mohammed Shariful and Acharya, U. Rajendra |
| Journal Title | Scientific Reports |
| Journal Citation | 13 (1) |
| Article Number | 960 |
| Number of Pages | 15 |
| Year | 2023 |
| Publisher | Nature Publishing Group |
| Place of Publication | United Kingdom |
| ISSN | 2045-2322 |
| Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-023-28188-w |
| Web Address (URL) | https://www.nature.com/articles/s41598-023-28188-w |
| Abstract | Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients’ age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients’ survival in the short- and long-term. |
| Keywords | Traumatic brain injury; algorithms; Glasgow coma s |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Shiraz University of Medical Sciences, Iran |
| Fasa University of Medical Sciences, Iran | |
| Fasa University, Iran | |
| Deakin University | |
| George Institute for Global Health, Australia | |
| University of Sydney | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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