Arterial Blood Gases Forecast Optimization by Artificial Neural Network Method

Wajs, Wieslaw; Wais, Piotr; Ochab, Marcin; Wojtowicz, Hubert

doi:10.1007/978-3-319-39796-2_36

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Alongside this problem, the research method chosen by the researchers in this study is a very time consuming and costly one [33]. Wajs et al (2016) researched smart blood gas prediction. They have been able to predict ABGs using neural network and ABG data obtained from repeated tests over a specified period.…”

Section: Ivdiscussionmentioning

confidence: 99%

“…The neural network used is a dynamic network that is retrained by receiving any data, which creates a computational time problem. It also seems that the model presented in this study is not comprehensive because of its dependence on the patient’s condition and requires the collection of preliminary data to construct it [35].…”

Section: Discussionmentioning

confidence: 99%

“…Honarmand and co-workers have also been able to establish a connection between the blood gases from the earlobe and the ABGs [34]. Concerning smart arterial gas prediction, Wajs et al have been able to predict arterial blood gas using neural network and arterial gas data obtained from repeated tests over specified time intervals [35].…”

Section: Introductionmentioning

confidence: 99%

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Use of artificial intelligence and neural network algorithms to predict arterial blood gas items in trauma victims

Shayan

Sabouri

Shayan

et al. 2020

Preprint

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Background: Trauma is the third leading cause of death in the world and the first cause of death among people younger than 44 years. In traumatic patients, especially those who are injured early in the day, arterial blood gas (ABG) is considered a golden standard because it can provide physicians with important information such as detecting the extent of internal injury, especially in the lung. However, measuring these gases by laboratory methods is a time-consuming task in addition to the difficulty of sampling the patient. The equipment needed to measure these gases is also expensive, which is why most hospitals do not have this equipment. Therefore, estimating these gases without clinical trials can save the lives of traumatic patients and accelerate their recovery. Methods: In this study, a method based on artificial neural networks for the aim of estimation and prediction of arterial blood gas is presented by collecting information about 2280 traumatic patients. In the proposed method, by training a feed-forward backpropagation neural network (FBPNN), the neural network can only predict the amount of these gases from the patient's initial information. The proposed method has been implemented in MATLAB software, and the collected data have tested its accuracy, and its results are presented. Results: The results show 87.92% accuracy in predicting arterial blood gas. The predicted arterial blood gases included PH, PCO2, and HCO3, which reported accuracy of 99.06%, 80.27%, and 84.43%, respectively. Therefore, the proposed method has relatively good accuracy in predicting arterial blood gas. Conclusions: Given that this is the first study to predict arterial blood gas using initial patient information(systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate (PR), respiratory rate (RR), and age), and based on the results, the proposed method could be a useful tool in assisting hospital and laboratory specialists, to be used. Keywords: Arterial Blood Gases, Trauma, Neural Network, Prediction.

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Section: Ivdiscussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Use of artificial intelligence and neural network algorithms to predict arterial blood gas items in trauma victims

Shayan

Sabouri

Shayan

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…They compared the results and concluded that the accuracy of the artificial network is higher [19]. Wajs et al could have predicted arterial blood gas by using neural and arterial gas data from experiments repeated over the specific periods of time [20].…”

Section: Neural Network Have Been Used For Five Decades To Analyze Mmentioning

confidence: 99%

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

Hafezi

Sabouri

Shayan

et al. 2020

Preprint

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Introduction: Knowing 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. Method: In 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. Results: Research 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 Conclusion: Due 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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Classify the Outcome of Arterial Blood Gas Test to Detect the Respiratory Failure Using Machine Learning

Kajanan

Kumara

Kuhaneswaran

et al. 2022

2022 International Conference on Decision Aid Sciences and Applications (DASA)

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Arterial Blood Gases Forecast Optimization by Artificial Neural Network Method

Cited by 4 publications

References 11 publications

Use of artificial intelligence and neural network algorithms to predict arterial blood gas items in trauma victims

Use of artificial intelligence and neural network algorithms to predict arterial blood gas items in trauma victims

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

Classify the Outcome of Arterial Blood Gas Test to Detect the Respiratory Failure Using Machine Learning

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