Acute and Critical Care

Original Article

Basic science and research
A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months: Mehdi Nourelahi, Fardad Dadboud, Hosseinali Khalili, Amin Niakan, Hossein Parsaei; Acute Crit Care. 2022;37(1):45-52. Published online January 21, 2022; DOI: https://doi.org/10.4266/acc.2021.00486

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Background
Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcomes in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings. Methods: In this study, we examined the capability of a machine learning-based model in predicting “favorable” or “unfavorable” outcomes after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI patients admitted to the neuro-intensive care unit of Rajaee (Emtiaz) Hospital (Shiraz, Iran) between 2015 and 2017. Model performance was evaluated using three indices: sensitivity, specificity, and accuracy. A ten-fold cross-validation method was used to estimate these indices. Results: Overall, the developed models showed excellent performance with the area under the curve around 0.81, sensitivity and specificity of around 0.78. The top-three factors important in predicting 6-month post-trauma survival status in TBI patients are “Glasgow coma scale motor response,” “pupillary reactivity,” and “age.” Conclusions: Machine learning techniques might be used to predict the 6-month outcome in TBI patients using only the parameters measured on admission when the machine learning is trained using a large data set.

Citations

Citations to this article as recorded by

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International Journal of Medical Informatics.2023; 178: 105201. CrossRef
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International Journal of Molecular Sciences.2023; 24(22): 16267. CrossRef
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Antonio Cerasa, Gennaro Tartarisco, Roberta Bruschetta, Irene Ciancarelli, Giovanni Morone, Rocco Salvatore Calabrò, Giovanni Pioggia, Paolo Tonin, Marco Iosa
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Review Article

Rapid response system
Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning: Youngnam Lee, Joon-myoung Kwon, Yeha Lee, Hyunho Park, Hugh Cho, Jinsik Park; Acute Crit Care. 2018;33(3):117-120. Published online August 31, 2018; DOI: https://doi.org/10.4266/acc.2018.00290

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531 Download
17 Web of Science
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Abstract PDF

With the wider adoption of electronic health records, the rapid response team initially believed that mortalities could be significantly reduced but due to low accuracy and false alarms, the healthcare system is currently fraught with many challenges. Rule-based methods (e.g., Modified Early Warning Score) and machine learning (e.g., random forest) were proposed as a solution but not effective. In this article, we introduce the DeepEWS (Deep learning based Early Warning Score), which is based on a novel deep learning algorithm. Relative to the standard of care and current solutions in the marketplace, there is high accuracy, and in the clinical setting even when we consider the number of alarms, the accuracy levels are superior.

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