Predicting the Need to Perform Life-Saving Interventions in Trauma Patients by Using New Vital Signs and Artificial Neural Networks

Background: Coronary angiography is gold standard method to diagnose coronary arteries diseases. The aim of this study was to estimate pain after coronary angiography based on vital signs for determining best position by using artiﬁcial neural networks ANN. Methodology: This study used a database containing 86 subjects that refer to angiography center. For each subject Vital signs were measured that included blood pressure, percent of blood oxygen saturation, heart rate, respiratory rate and temperature. The Numeric Rating scale (NRS) was used to determine pain intensity. The vital signs were the inputs and the pain value was the corresponding output. These data were applied to train the ANN in the learning process. The model was implemented in MATLAB software. The results of pain estimation were compared with the results of NRS method and the error rate was calculated. Results: The absolute error and error percentage between NRS method and the present method were 5.41 ± 2.63 mmHg, 4.09 ± 1.59%. The results indicated that the pain measurement by NRS method and pain value predicted with trained ANN differ by only less than 11%. It is obvious that the neural network prediction fit properly to the NRS results. Conclusion: The results of proposed method were closely in agreement with the results of the NRS. so this method can be suggested for reliving the pain and determining the best patient's position after the angiography procedure. Key words: Artiﬁcial neural network; Coronary angiography; Pain Citation: Heravi MAY, Yaghubi MS, Amini ZA, Salimi PS, Falahi ZZ, Gazerani AG. Pain estimation after coronary angiography based on vital signs by using artiﬁcial neural networks. Anaesth. pain intensive care 2021;25(1):27–32. DOI: 10.35975/apic.v25i1.1433 Received: 21 November 2020, Reviewed: 2 December 2020, Accepted: 12 December 2020

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Development of a model for prediction of survival in pediatric trauma patients: Comparison of artificial neural networks and logistic regression

Journal of Pediatric Surgery ◽

10.1053/jpsu.2002.33885 ◽

2002 ◽

Vol 37 (7) ◽

pp. 1098-1104 ◽

Cited By ~ 20

Author(s):

Stephen M. DiRusso ◽

A.Alfred Chahine ◽

Thomas Sullivan ◽

Donald Risucci ◽

Peter Nealon ◽

...

Keyword(s):

Neural Networks ◽

Logistic Regression ◽

Artificial Neural Networks ◽

Pediatric Trauma ◽

Trauma Patients ◽

Prediction Of Survival ◽

Artificial Neural

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Investigating the effect of respiratory indices to predict mortality and the status of trauma patients using artificial neural networks

10.1101/2020.06.30.20143784 ◽

2020 ◽

Author(s):

Zahra Hafezi ◽

Mohammad Sabouri ◽

Milad Shayan ◽

Shahram Paydar

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Artificial Neural Networks ◽

Data Science ◽

Medical Center ◽

Care Process ◽

Trauma Patients ◽

Research Subjects ◽

The Status ◽

Artificial Neural

AbstractIntroductionKnowing the final status of trauma patients and clearly understanding their condition are always significant due to the complexity of injuries and the high dependence of the patient’s condition on various factors. That is why it allows doctors to be able to provide required facilities in a broader perspective and perform appropriate action. In addition, it can avoid wasting time and energy and then increasing patient mortality. While, there are several ways to measure and predict the final status of patients but all of them have some defects. Therefore, it is very important for the significance of the system design with high accuracy and reliability to be able to help physicians investigate the final status of trauma patients.MethodIn this study, a method, a sub-branch of data science and artificial intelligence, is presented and studied based on artificial neural networks to estimate the final status of trauma patients and predict their survival and death probabilities during the treatment and care process. In the proposed method, the final status of patients is predicted using 13 respiratory indices. This method is run in Matlab and its efficiency is studied.ResultsResearch subjects include 3073 patients, 494 females and 2579 males, from Shahid Rajaei Medical Center in Shiraz. In general, according to the results from testing the method, it has been able to accurately predict the mortality of patients based on respiratory indices. The proposed structure has been able to predict patients’ survival and death probabilities with an accuracy of %73.75 and %99.71 respectively. Therefore, we can conclude that the presented and examined method can make a significant relevance between calculated respiratory indices and final status of patients.Discussion and ConclusionDue to the present study and the obtained results and investigating the mortality relevance with the other 13 respiratory indices using artificial intelligence-based methods, it can be stated that these indicators are good criteria for predicting mortality.

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Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

BioMed Research International ◽

10.1155/2015/536863 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 11

Author(s):

Muammar Sadrawi ◽

Shou-Zen Fan ◽

Maysam F. Abbod ◽

Kuo-Kuang Jen ◽

Jiann-Shing Shieh

Keyword(s):

Blood Pressure ◽

Neural Networks ◽

Artificial Neural Networks ◽

Cross Validation ◽

Vital Signs ◽

Eeg Signal ◽

Depth Of Anesthesia ◽

Ann Model ◽

Mean Values ◽

Artificial Neural

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.

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