Clinical decision support for severe trauma patients: Machine learning based definition of a bundle of care for hemorrhagic shock and traumatic brain injury

Lang, Elodie; Neuschwander, Arthur; Favé, Gersende; Abback, Paër-Sélim; Esnault, Pierre; Geeraerts, Thomas; Harrois, Anatole; Hanouz, Jean‐Luc; Kipnis, Éric; Léone, Marc; Legros, Vincent; Mellati, Nouchan; Pottecher, Julien; Hamada, Sophie; Pirracchio, Romain

doi:10.1097/ta.0000000000003401

Cited by 11 publications

(10 citation statements)

References 37 publications

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“…In line with our results, in 2019, patients that could decrease the 7-day mortality of these patients. This nding highlights the value of ML algorithms for application in critical clinical decision makings 44 .…”

Section: 46mentioning

confidence: 85%

Survival Prediction in Traumatic Brain Injury Patients Using Machine Learning Algorithms

Khalili

Rismani

Nematollahi

et al. 2022

Preprint

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Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning 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 machine learning 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, condition of pupils, and 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 had 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 machine learning algorithms can provide a reliable prediction of TBI patients’ survival in the short- and long-term with reliable and easily accessible features of patients.

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Section: 46mentioning

confidence: 85%

Survival Prediction in Traumatic Brain Injury Patients Using Machine Learning Algorithms

Khalili

Rismani

Nematollahi

et al. 2022

Preprint

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“…The race toward achieving reliable ML model for robust clinical decision-making continues 53 . For example, Lang et al provided clinical decision support for TBI patients capable of reducing the 7-day mortality showing the ML potential in clinical decision makings 54 . On the contrary, ML failed to outperform LR in predicting the outcome of a large database of patients with moderate to severe TBI 55 .…”

Section: Discussionmentioning

confidence: 99%

Prognosis prediction in traumatic brain injury patients using machine learning algorithms

Khalili

Rismani

Nematollahi

et al. 2023

Sci Rep

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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.

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“…Eighteen studies did not provide any information on any validation performed on the model. Twelve studies utilized a secondary cohort from a different database as a testing set for the models [ 45 , 57 , 58 , 61 , 68 , 73 , 88 , 90 , 93 , 96 , 110 , 115 ]. Finally, out of the included studies, four studies performed an external validation on a previously developed ML model [ 31 , 47 , 61 , 115 ].…”

Section: Application Of ML Algorithms For Hemorrhagic Traumamentioning

confidence: 99%

“…In terms of imputation methods, mean imputation was the most used among the 33 studies which mention how the missing values were handled. Other imputation methods used were iterative or multiple imputation, ElasticNet regression, optimal imputation, chained equation imputation, and median imputation [ 30 , 35 , 44 , 62 , 70 , 71 , 80 , 94 , 97 , 110 , 113 ]. For dealing with imbalanced data, 6 studies addressed it with the most commonly used method being Synthetic Minority Over-Sampling Technique [ 49 , 63 , 72 , 81 , 91 , 99 ].…”

Section: Application Of ML Algorithms For Hemorrhagic Traumamentioning

confidence: 99%

Artificial intelligence and machine learning for hemorrhagic trauma care

et al. 2023

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Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and contribute to future development of ML in trauma care, we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage. A literature search was carried out on PubMed and Google scholar. Titles and abstracts were screened and, if deemed appropriate, the full articles were reviewed. We included 89 studies in the review. These studies could be grouped into five areas: (1) prediction of outcomes; (2) risk assessment and injury severity for triage; (3) prediction of transfusions; (4) detection of hemorrhage; and (5) prediction of coagulopathy. Performance analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML models. However, most studies were retrospective, focused on prediction of mortality, and development of patient outcome scoring systems. Few studies performed model assessment via test datasets obtained from different sources. Prediction models for transfusions and coagulopathy have been developed, but none is in widespread use. AI-enabled ML-driven technology is becoming integral part of the whole course of trauma care. Comparison and application of ML algorithms using different datasets from initial training, testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.

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Clinical decision support for severe trauma patients: Machine learning based definition of a bundle of care for hemorrhagic shock and traumatic brain injury

Cited by 11 publications

References 37 publications

Survival Prediction in Traumatic Brain Injury Patients Using Machine Learning Algorithms

Survival Prediction in Traumatic Brain Injury Patients Using Machine Learning Algorithms

Prognosis prediction in traumatic brain injury patients using machine learning algorithms

Artificial intelligence and machine learning for hemorrhagic trauma care

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