The trauma severity model: An ensemble machine learning approach to risk prediction

Gorczyca, Michael T.; Toscano, Nicole; Cheng, Julius D.

doi:10.1016/j.compbiomed.2019.02.025

Cited by 19 publications

(12 citation statements)

References 44 publications

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“…Comparing the results to TRISS, the sensitivity and specificity of TRISS showed better results with 81% and 95%, respectively ( 24 ). In 2019, Gorczyca et al were able to achieve excellent classification rates for mortality prediction, comparing a state-of-the-art ANN to several other prediction models including Bayesian networks, ISS, and others ( 29 ). Hubbard et al used SL to predict mortality and the need for transfusion within discrete time intervals (30–90, 90–180, and 180–360 min) in patients meeting the criteria for highest-level trauma activation in 10 major Level I hospitals.…”

Section: Resultsmentioning

confidence: 99%

Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Baur

Gehlen²,

Scherer³

et al. 2022

Front. Surg.

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IntroductionTreating severely injured patients requires numerous critical decisions within short intervals in a highly complex situation. The coordination of a trauma team in this setting has been shown to be associated with multiple procedural errors, even of experienced care teams. Machine learning (ML) is an approach that estimates outcomes based on past experiences and data patterns using a computer-generated algorithm. This systematic review aimed to summarize the existing literature on the value of ML for the initial management of severely injured patients.MethodsWe conducted a systematic review of the literature with the goal of finding all articles describing the use of ML systems in the context of acute management of severely injured patients. MESH search of Pubmed/Medline and Web of Science was conducted. Studies including fewer than 10 patients were excluded. Studies were divided into the following main prediction groups: (1) injury pattern, (2) hemorrhage/need for transfusion, (3) emergency intervention, (4) ICU/length of hospital stay, and (5) mortality.ResultsThirty-six articles met the inclusion criteria; among these were two prospective and thirty-four retrospective case series. Publication dates ranged from 2000 to 2020 and included 32 different first authors. A total of 18,586,929 patients were included in the prediction models. Mortality was the most represented main prediction group (n = 19). ML models used were artificial neural network ( n = 15), singular vector machine (n = 3), Bayesian network (n = 7), random forest (n = 6), natural language processing (n = 2), stacked ensemble classifier [SuperLearner (SL), n = 3], k-nearest neighbor (n = 1), belief system (n = 1), and sequential minimal optimization (n = 2) models. Thirty articles assessed results as positive, five showed moderate results, and one article described negative results to their implementation of the respective prediction model.ConclusionsWhile the majority of articles show a generally positive result with high accuracy and precision, there are several requirements that need to be met to make the implementation of such models in daily clinical work possible. Furthermore, experience in dealing with on-site implementation and more clinical trials are necessary before the implementation of ML techniques in clinical care can become a reality.

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Section: Resultsmentioning

confidence: 99%

Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Baur

Gehlen²,

Scherer³

et al. 2022

Front. Surg.

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“…Table 2 represents a brief at the same time complete comparison for single and hybrid methods in terms of accuracy, reliability, and sustainability. [76] developed a Trauma Severity model as an ensemble machine learning for risk estimation. This method has been compared with the Harborview Assessment for Risk of Mortality, Bayesian Logistic Injury Severity Score, and the Trauma Mortality Prediction Model in terms of accuracy and F-score values.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods<strong> </strong>

Ardabili¹,

Mosavi²,

Várkonyi-Kóczy³

2019

Preprint

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The conventional machine learning (ML) algorithms are continuously advancing and evolving at a fast-paced by introducing the novel learning algorithms. ML models are continually improving using hybridization and ensemble techniques to empower computation, functionality, robustness, and accuracy aspects of modeling. Currently, numerous hybrid and ensemble ML models have been introduced. However, they have not been surveyed in a comprehensive manner. This paper presents the state of the art of novel ML models and their performance and application domains through a novel taxonomy.

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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%

“…Model performance metrics varied depending on the outcome being predicted, ML method used and the prediction window. Some studies developed additional models and/or used trauma/injury scoring standards to compare and evaluate the performance of the developed algorithm [ 27 , 28 , 30 , 34 , 36 , 37 , 41 , 61 , 65 , 67 , 71 , 73 , 75 – 77 , 80 , 83 , 85 , 88 – 90 , 93 , 108 , 113 ].…”

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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The trauma severity model: An ensemble machine learning approach to risk prediction

Cited by 19 publications

References 44 publications

Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods<strong> </strong>

Artificial intelligence and machine learning for hemorrhagic trauma care

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