Artificial Neural Networks Prognostic Evaluation of Post-Surgery Complications in Patients Underwent to Coronary Artery Bypass Graft Surgery

Souza, César Roberto de; Pizzolato, Ednaldo Brigante; Mendes, Renata Gonçalves; Borghi‐Silva, Audrey; Machado, Maurício de Nassau; Corrêa, Paulo Rogério

doi:10.1109/icmla.2009.116

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…Reintubation and PMV increase are associate with airway and lung trauma increased morbidity (ventilator-associated pneumonia, intensive care unit length of stay, resource utilization and health care costs) and mortality [ 14 , 15 , 27 ]. In this context, the greatest difficulty for health care professionals is to predict the risk for adverse events early in the process.…”

Section: Discussionmentioning

confidence: 99%

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Clinical research Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models

Mendes¹,

Souza²,

Machado³

et al. 2015

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IntroductionIn coronary artery bypass (CABG) surgery, the common complications are the need for reintubation, prolonged mechanical ventilation (PMV) and death. Thus, a reliable model for the prognostic evaluation of those particular outcomes is a worthwhile pursuit. The existence of such a system would lead to better resource planning, cost reductions and an increased ability to guide preventive strategies. The aim of this study was to compare different methods – logistic regression (LR) and artificial neural networks (ANNs) – in accomplishing this goal.Material and methodsSubjects undergoing CABG (n = 1315) were divided into training (n = 1053) and validation (n = 262) groups. The set of independent variables consisted of age, gender, weight, height, body mass index, diabetes, creatinine level, cardiopulmonary bypass, presence of preserved ventricular function, moderate and severe ventricular dysfunction and total number of grafts. The PMV was also an input for the prediction of death. The ability of ANN to discriminate outcomes was assessed using receiver-operating characteristic (ROC) analysis and the results were compared using a multivariate LR.ResultsThe ROC curve areas for LR and ANN models, respectively, were: for reintubation 0.62 (CI: 0.50–0.75) and 0.65 (CI: 0.53–0.77); for PMV 0.67 (CI: 0.57–0.78) and 0.72 (CI: 0.64–0.81); and for death 0.86 (CI: 0.79–0.93) and 0.85 (CI: 0.80–0.91). No differences were observed between models.ConclusionsThe ANN has similar discriminating power in predicting reintubation, PMV and death outcomes. Thus, both models may be applicable as a predictor for these outcomes in subjects undergoing CABG.

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Section: Discussionmentioning

confidence: 99%

“…In a study by Souza et al . [ 15 ], the complexity of the networks was controlled using early stopping. Early stopping can be considered a form of regularization of complexity control.…”

Section: Methodsmentioning

confidence: 99%

Clinical research Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models

Mendes¹,

Souza²,

Machado³

et al. 2015

aoms

Self Cite

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“…The comparison between the two types of neural networks highlighted the superiority of the radial basis network over the Bayesian; both demonstrating high relative accuracy at 98% and 90% respectively. AI was also used to implement a monitoring system that conducted prognosis of postsurgical complications [26], for patients who had undergone coronary artery bypass grafting (CABG). The AI tool used in this particular instance was a single hidden layer artificial neural network [26].…”

Section: Furthermore Interesting Approaches Have Been Implementedmentioning

confidence: 99%

“…AI was also used to implement a monitoring system that conducted prognosis of postsurgical complications [26], for patients who had undergone coronary artery bypass grafting (CABG). The AI tool used in this particular instance was a single hidden layer artificial neural network [26]. Prognosis algorithms of post-cardiac surgery complications were benchmarked for performance accuracy in a recent review of the field [27].…”

Section: Furthermore Interesting Approaches Have Been Implementedmentioning

confidence: 99%

Artificial Intelligence for Developing Tools and Technologies in Vascular and Cardiac Surgery Applications: A Survey

Alamaniotis¹

2021

AJBSR

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“…Currently, researchers have exposed their interest in the health area, as there is a huge chance to support human lives by monitoring or detecting diseases. Monitoring postsurgery patients is a positive research area because regular monitoring of the patient's health status is compulsory to prevent any loss [9,10]. Ankle fracture is one of the most common in bone and joint injuries.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information

et al. 2020

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In the past decades, cognitive computing and communication densely used in lots of networking areas. Current improvement in deep learning (DL) and big data analysis create great potential to analyze cognitive intelligence (CI) for many applications such as human activity monitoring and recognition through wireless communication. Cognitive intelligence and wireless communication are using to establish smart healthcare systems. Healthcare monitoring systems turn into interesting research subjects where monitoring post-operative surgical patients are the current focal point to the researcher. In this paper, we argue that deep learning along with the wireless communication technique introduces cognitive intelligence for the healthcare monitoring system. We present a deep learning based convolutional neural network (CNN) model to classify image data and a convenient and multi-functional software-defined radio (SDR) platform to detect movement of the ankle of patients who underwent ankle fracture surgery. Capturing wireless channel state information (WCSI) in the presence of the human body and classifying using CNN to observe distinct movements is the key idea of this study. A universal software radio peripheral (USRP) platform used to capture WCSI data and used for classification. AlexNet and ZFNet both are the famous architecture of CNN and used in a parallel way to classify captured WCSI-based images that converted from numeric data. The classification established on the ankles movements after surgery and classification results show that CNN provides satisfying results where test accuracy is 98.98%.

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Artificial Neural Networks Prognostic Evaluation of Post-Surgery Complications in Patients Underwent to Coronary Artery Bypass Graft Surgery

Cited by 5 publications

References 22 publications

Clinical research Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models

Clinical research Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models

Artificial Intelligence for Developing Tools and Technologies in Vascular and Cardiac Surgery Applications: A Survey

Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information

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