Introduction

Conrady, Roland; Ruetz, David; Aeberhard, Marc

doi:10.1007/978-3-030-59893-8_1

Cited by 2 publications

(7 citation statements)

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“…We trained the model using data from 2017, recalibrated it using data from 2018, and tested it using data from 2019. To address missing data during model development, we selected the structural expectation maximization algorithm to impute missing variables in the training data set 17,19 . Five variables among those used for training were missing in >20% of records: prehospital systolic blood pressure, heart rate, respiratory rate, oxygen saturation, and GCS.…”

Section: Methodsmentioning

confidence: 99%

“…Continuous variables were discretized using the decision tree algorithm, including age, systolic blood pressure, heart rate, respiratory rate, oxygen saturation, GCS, and the number of body regions injured 17 . We applied three supervised learning algorithms to define the machine-driven direct and indirect causal relationships between the need for blood transfusion and the selected variables: Naïve Bayes, Markov Blanket, and Augmented Markov Blanket 17 . Structural priors learning was used to maximize the number of predictors and their connections 17 …”

Section: Methodsmentioning

confidence: 99%

“…Predictor variables were selected for inclusion in the final model based on the variable's amount of information provided to predict the probability of the dependent variable (blood transfusion within 4 hours). The amount of information provided by each variable to prediction is measured using mutual information 17,20 . We ranked the contribution of each variable in the final model as a predictor of the dependent variable using relative mutual information (RMI), the percentage equivalent of mutual information 17 .…”

Section: Methodsmentioning

confidence: 99%

“…We applied three supervised learning algorithms to define the machine-driven direct and indirect causal relationships between the need for blood transfusion and the selected variables: Naïve Bayes, Markov Blanket, and Augmented Markov Blanket 17 . Structural priors learning was used to maximize the number of predictors and their connections 17 …”

Section: Methodsmentioning

confidence: 99%

“…A Bayesian belief network is a model that represents the joint probability distribution for a set of variables 16 . This modeling approach allows the estimation of the probability of a dependent variable using observed or unobserved values of each independent variable 16,17 . We used the software package BayesiaLab (Franklin, TN) to train a Bayesian belief network to predict if a patient receives a packed red blood cell transfusion within 4 hours after arrival to the hospital.…”

Section: Methodsmentioning

confidence: 99%

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Development and validation of a Bayesian belief network predicting the probability of blood transfusion after pediatric injury

Sullivan

Milestone

Tempel

et al. 2022

J Trauma Acute Care Surg

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BACKGROUND:Early recognition and intervention of hemorrhage are associated with decreased morbidity in children. Triage models have been developed to aid in the recognition of hemorrhagic shock after injury but require complete data and have limited accuracy. To address these limitations, we developed a Bayesian belief network, a machine learning model that represents the joint probability distribution for a set of observed or unobserved independent variables, to predict blood transfusion after injury in children and adolescents. METHODS:We abstracted patient, injury, and resuscitation characteristics of injured children and adolescents (age 1 to 18 years) from the 2017 to 2019 Trauma Quality Improvement Project database. We trained a Bayesian belief network to predict blood transfusion within 4 hours after arrival to the hospital following injury using data from 2017 and recalibrated the model using data from 2018. We validated our model on a subset of patients from the 2019 Trauma Quality Improvement Project. We evaluated model performance using the area under the receiver operating characteristic curve and calibration curves and compared performance with pediatric age-adjusted shock index (SIPA) and reverse shock index with Glasgow Coma Scale (rSIG) using sensitivity, specificity, accuracy, and Matthew's correlation coefficient (MCC). RESULTS:The final model included 14 predictor variables and had excellent discrimination and calibration. The model achieved an area under the receiver operating characteristic curve of 0.92 using emergency department data. When used as a binary predictor at an optimal threshold probability, the model had similar sensitivity, specificity, accuracy, and MCC compared with SIPA when only age, systolic blood pressure, and heart rate were observed. With the addition of the Glasgow Coma Scale score, the model has a higher accuracy and MCC than SIPA and rSIG. CONCLUSION:A Bayesian belief network predicted blood transfusion after injury in children and adolescents better than SIPA and rSIG. This probabilistic model may allow clinicians to stratify hemorrhagic control interventions based upon risk.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Development and validation of a Bayesian belief network predicting the probability of blood transfusion after pediatric injury

Sullivan

Milestone

Tempel

et al. 2022

J Trauma Acute Care Surg

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In search of elite travel: visual communication of luxury hotels

Michael,

Fusté-Forné

2023

CBTH

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Purpose The relationships between luxury and tourism are manifested in meaningful customer experiences which are focused on exclusive products and services. This paper aims to examine the visual communication of hotels in the Middle East to identify what luxury features are represented. Design/methodology/approach The digital communication of four luxury hotels in the United Arab Emirates was analysed based on a visual content and semiotic analysis of their Instagram’s posts (N = 1,866). Findings Results show that customers and views of the hotel and from the hotel are the most representative features of the social media communication of luxury hotels. Originality/value Theoretical and practical implications derived from the results are described, and opportunities for further research are identified in relation to digital marketing communication and the effect on consumers in hospitality and tourism.

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Introduction

Cited by 2 publications

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Development and validation of a Bayesian belief network predicting the probability of blood transfusion after pediatric injury

Development and validation of a Bayesian belief network predicting the probability of blood transfusion after pediatric injury

In search of elite travel: visual communication of luxury hotels

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