Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence

Paydar, Shahram; Parva, Elahe; Ghahramani, Zahra; Pourahmad, Saeedeh; Shayan, Leila; Mohammadkarimi, Vahid; Sabetian, Golnar

doi:10.1016/j.cjtee.2020.11.009

Cited by 11 publications

(10 citation statements)

References 16 publications

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“…For this purpose, paraclinical and clinical data were extracted. Diastolic blood pressure, GCS, and BE after resuscitation crystallized as the most impactful parameters; the outcome was predicted with high accuracy of 0.99 ( 11 ).…”

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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“…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%

“…Kilic et al [ 28 ] and Pearl et al [ 78 ] found that physiological variables from the scene had little to no impact on the performance of their model; Kilic also noted that response to resuscitation had an important effect on trauma mortality [ 28 , 78 ]. Paydar et al [ 97 ] reported that DBP was more important than SBP as a predictor for mortality, while Walczak et al [ 103 ] found that SBP was the second most contributing variable for transfusion prediction.…”

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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“…Previous studies have applied PCA to detect patterns reflecting dysregulation of the coagulation system using laboratory parameters 8–10 and to predict early organ dysfunction in trauma patients using circulating inflammatory mediators 11 . In addition, there has been a recent push to apply data-driven computational methods to complex problems like trauma triage 12 . However, to our knowledge, the use of PCA as a predictive tool in pediatric trauma has not yet been investigated.…”

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confidence: 99%

Prehospital factors predict outcomes in pediatric trauma: A principal component analysis

Ting

Wakeman

Arca

et al. 2022

J Trauma Acute Care Surg

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BACKGROUND:Trauma team activation leveling decisions are complex and based on many variables. Accurate triage decisions improve patient safety and resource utilization. Our purpose was to establish proof-of-concept for using principal component analysis (PCA) to identify multivariate predictors of injury severity and to assess their ability to predict outcomes in pediatric trauma patients. We hypothesized that we could identify significant principal components (PCs) among variables used for decisions regarding trauma team activation and that PC scores would be predictive of outcomes in pediatric trauma. METHODS:We conducted a retrospective review of the trauma registry (January 2014 to December 2020) at our pediatric trauma center, including all pediatric patients (age <18 years) who triggered a trauma team activation. Data included patient demographics, prehospital report, Injury Severity Score, and outcomes. Four significant principal components were identified using PCA. Differences in outcome variables between the highest and lowest quartile for PC score were examined. RESULTS:There were 1,090 pediatric patients included. The four significant PCs accounted for greater than 96% of the overall data variance.The first PC was a composite of prehospital Glasgow Coma Scale and Revised Trauma Score and was predictive of outcomes, including injury severity, length of stay, and mortality. The second PC was characterized primarily by prehospital systolic blood pressure and high PC scores were associated with increased length of stay. The third and fourth PCs were characterized by patient age and by prehospital Revised Trauma Score and systolic blood pressure, respectively. CONCLUSION:We demonstrate that, using information available at the time of trauma team activation, PCA can be used to identify key predictors of patient outcome. While the ultimate goal is to create a machine learning-based predictive tool to support and improve clinical decision making, this study serves as a crucial step toward developing a deep understanding of the features of the model and their behavior with actual clinical data.

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Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence

Cited by 11 publications

References 16 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

Artificial intelligence and machine learning for hemorrhagic trauma care

Prehospital factors predict outcomes in pediatric trauma: A principal component analysis

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