Using an Artificial Neural Networks (ANNs) Model for Prediction of Intensive Care Unit (ICU) Outcome and Length of Stay at Hospital in Traumatic Patients

Gholipour, Changiz; Rahim, Fakher; Fakhree, A M Bassir; Ziapour, Behrad

doi:10.7860/jcdr/2015/9467.5828

Cited by 34 publications

(33 citation statements)

References 14 publications

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“…94 For example, neural networks have been used successfully for breathing-pattern recognition in critical care, weaning from mechanical ventilation, and ICU outcomes prediction. [95][96][97] Likewise, neural networks may be able to recognize other types of respiratory patterns during periods of poor patient-ventilator interaction. A neural network may be fed with respiratory waveforms (flow, pressure, or both) and trained to recognize normal and asynchronous breaths.…”

Section: Monitoring Asynchronies In the Era Of Precision Medicinementioning

confidence: 99%

Minimizing Asynchronies in Mechanical Ventilation: Current and Future Trends

et al. 2018

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Patient-ventilator asynchrony exists when the phases of breath delivered by the ventilator do not match those of the patient. Asynchronies occur throughout mechanical ventilation and negatively affect patient comfort, duration of mechanical ventilation, length of ICU stays, and mortality. Identifying asynchronies requires careful attention to patients and their ventilator waveforms. This review discusses the different types of asynchronies, how they are generated, and their impact on patient comfort and outcome. Moreover, it discusses practical approaches for detecting, correcting, and preventing asynchronies. Current evidence suggests that the best approach to managing asynchronies is by adjusting ventilator settings. Proportional modes improve patient-ventilator coupling, resulting in greater comfort and less dyspnea, but not in improved outcomes with respect to the duration of mechanical ventilation, delirium, or cognitive impairment. Advanced computational technologies will allow smart alerts, and models based on time series of asynchronies will be able to predict and prevent asynchronies, making it possible to tailor mechanical ventilation to meet each patient's needs throughout the course of mechanical ventilation.

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Section: Monitoring Asynchronies In the Era Of Precision Medicinementioning

confidence: 99%

Minimizing Asynchronies in Mechanical Ventilation: Current and Future Trends

et al. 2018

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“…ANN models can be trained and refined, randomly assigning relative weight to each input variable to construct the most robust prediction. 3,5,6 Although ANNs are considered "black-box" computational models, their value in clinical medicine has enormous potential to engage in evidence-based medical practice because they can be trained on new patient information. ANN models also benefit from internal validation and testing and tend to have much stronger predictive ability of binary outcomes compared to multivariate regression modeling.…”

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confidence: 99%

Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury

Hale

Stonko

Brown

et al. 2018

Neurosurgical Focus

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OBJECTIVEModern surgical planning and prognostication requires the most accurate outcomes data to practice evidence-based medicine. For clinicians treating children following traumatic brain injury (TBI) these data are severely lacking. The first aim of this study was to assess published CT classification systems in the authors’ pediatric cohort. A pediatric-specific machine-learning algorithm called an artificial neural network (ANN) was then created that robustly outperformed traditional CT classification systems in predicting TBI outcomes in children.METHODSThe clinical records of children under the age of 18 who suffered a TBI and underwent head CT within 24 hours after TBI (n = 565) were retrospectively reviewed.RESULTS“Favorable” outcome (alive with Glasgow Outcome Scale [GOS] score ≥ 4 at 6 months postinjury, n = 533) and “unfavorable” outcome (death at 6 months or GOS score ≤ 3 at 6 months postinjury, n = 32) were used as the primary outcomes. The area under the receiver operating characteristic (ROC) curve (AUC) was used to delineate the strength of each CT grading system in predicting survival (Helsinki, 0.814; Rotterdam, 0.838; and Marshall, 0.781). The AUC for CT score in predicting GOS score ≤ 3, a measure of overall functionality, was similarly predictive (Helsinki, 0.717; Rotterdam, 0.748; and Marshall, 0.663). An ANN was then constructed that was able to predict 6-month outcomes with profound accuracy (AUC = 0.9462 ± 0.0422).CONCLUSIONSThis study showed that machine-learning can be leveraged to more accurately predict TBI outcomes in children.

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“…These models have classically relied on logistic regression or conventional statistics to generate predictions, and often they use fewer input variables that are manually entered. More recently, ANNs have been shown to robustly predict complications, outcomes, and prognosis among numerous fields, 5,13,31,[34][35][36] including TBI. 8,18,26,32,37,38 Thus, an ANN tool yielding predictive information concerning CRTBI would be helpful and provide an evidence-based mechanism for treating these patients.…”

Section: Discussionmentioning

confidence: 99%

“…5,6,9 They are often more useful than conventional statistical methods because: 1) ANNs can take any number of input variables and predict any number of outcomes; 2) they are capable of improving their predictive ability over time as they are exposed to new data; 3) they benefit from internal validation and testing; and 4) they tend to have stronger discriminant ability compared to conventional statistics. 1,13,31,40 Leveraging this technology, we created a model that combines clinical and radiologist-interpreted CT data to predict whether or not a pediatric patient will experience a CRTBI. We quantify the accuracy and error of this algorithm and provide an open-source software package to enable prediction generation and validation.…”

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confidence: 99%

Using an artificial neural network to predict traumatic brain injury

Hale

Stonko

Lim

et al. 2019

Journal of Neurosurgery: Pediatrics

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OBJECTIVEPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling in patients who will have a clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based way to safely discharge children who are at low risk for a CRTBI. The authors hypothesized that an artificial neural network (ANN) trained on clinical and radiologist-interpreted imaging metrics could provide a tool for identifying patients likely to suffer from a CRTBI.METHODSThe authors used the prospectively collected, publicly available, multicenter Pediatric Emergency Care Applied Research Network (PECARN) TBI data set. All patients under the age of 18 years with TBI and admission head CT imaging data were included. The authors constructed an ANN using clinical and radiologist-interpreted imaging metrics in order to predict a CRTBI, as previously defined by PECARN: 1) neurosurgical procedure, 2) intubation > 24 hours as direct result of the head trauma, 3) hospitalization ≥ 48 hours and evidence of TBI on a CT scan, or 4) death due to TBI.RESULTSAmong 12,902 patients included in this study, 480 were diagnosed with CRTBI. The authors’ ANN had a sensitivity of 99.73% with precision of 98.19%, accuracy of 97.98%, negative predictive value of 91.23%, false-negative rate of 0.0027%, and specificity for CRTBI of 60.47%. The area under the receiver operating characteristic curve was 0.9907.CONCLUSIONSThe authors are the first to utilize artificial intelligence to predict a CRTBI in a clinically meaningful manner, using radiologist-interpreted CT information, in order to identify pediatric patients likely to suffer from a CRTBI. This proof-of-concept study lays the groundwork for future studies incorporating iterations of this algorithm directly into the electronic medical record for real-time, data-driven predictive assistance to physicians.

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Using an Artificial Neural Networks (ANNs) Model for Prediction of Intensive Care Unit (ICU) Outcome and Length of Stay at Hospital in Traumatic Patients

Abstract: Using ANN model based on clinical and biochemical variables in patients with moderate to severe traumatic injury, resulted in satisfactory outcome prediction when applied to a test set.

Cited by 34 publications

References 14 publications

Minimizing Asynchronies in Mechanical Ventilation: Current and Future Trends

Minimizing Asynchronies in Mechanical Ventilation: Current and Future Trends

Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury

Using an artificial neural network to predict traumatic brain injury

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