A neural network approach to predicting outcomes in heart failure using cardiopulmonary exercise testing

Myers, Jonathan; Souza, César Roberto de; Borghi‐Silva, Audrey; Guazzi, Marco; Chase, Paul; Bensimhon, Daniel; Peberdy, Mary Ann; Ashley, Euan A.; West, Erin; Cahalin, Lawrence P.; Forman, Daniel E.; Arena, Ross

doi:10.1016/j.ijcard.2013.12.031

Cited by 44 publications

(28 citation statements)

References 37 publications

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“…The symptoms consist of chest pain, arm or jaw pain, breathlessness, nausea, vomiting, sweating, dizziness and syncope (Cannon et al 2001). However, chest pain is considered as the major symptom of ACS in both men and women (Arslanian-Engoren et al 2006;Canto et al 2000;DeVon and Ryan 2005;Zdzienicka et al 2007;Davidson et al 2010;National Collaborating Centre for Chronic Conditions 2003;Fuster and Kovacic 2014;Myers et al 2014).…”

Section: Uncertainties Of Acs Signs and Symptomsmentioning

confidence: 99%

A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty

et al. 2017

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Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support vector machine. Hence, it can be argued that the BRBES is capable of playing an important role in decision making as well as in avoiding costly laboratory investigations. A procedure to train the system, allowing its enhancement of performance, is also presented.

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Section: Uncertainties Of Acs Signs and Symptomsmentioning

confidence: 99%

A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty

et al. 2017

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“…In summary, effective applications of artificial neural network (ANN) and logistic regression to predict medical outcomes have been proved in many works [25,27,28,31]. However, a challenge remains still in securing an effective analytic tool with high accuracy to help make evidence-based decisions with quality efficiently.…”

Section: Related Workmentioning

confidence: 99%

“…The assessment and prediction of various diseases for patients have been widely reviewed by using data mining techniques and statistical tools [4,5,6,7,8,25]. Especially, there have been a number of the predictive models which employ artificial neural network and regressions for predicting medical outcomes in various chronical diseases [25,27,28,31]. However, a challenge remains still in securing an effective analytic tool with high accuracy to help support personalized evidence-based decisions.…”

Section: Introductionmentioning

confidence: 99%

An intelligent recommender system based on predictive analysis in telehealthcare environment

Lafta

Zhang

Tao

et al. 2016

WEB

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Abstract. The use of intelligent technologies for providing useful recommendations to patients suffering chronic diseases may play a positive role in improving the general life quality of patients and help reduce the workload and cost involved in their daily healthcare. The objective of this study is to develop an intelligent recommender system based on predictive analysis for advising patients in the telehealth environment concerning whether they need to take the body test one day in advance by analyzing medical measurements of a patient for the past k days. The proposed algorithms supporting the recommender system have been validated using a time series telehealth data recorded from heart disease patients which were collected from May to January 2012, from our industry collaborator Tunstall. The experimental results show that the proposed system yields satisfactory recommendation accuracy and offer a promising way for saving the workload for patients to conduct body tests every day. This study highlights the possible usefulness of the computerized analysis of time series telehealth data in providing appropriate recommendations to patients suffering chronic diseases such as heart diseases patients.

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“…As a result, once trained, the ANN can recognize patterns or make predictions on those data that are not selected for training or are unknown to the ANN (12,16). ANNs as a datamodeling method have been studied in different areas of medical applications (3,13,14,(17)(18)(19)(20). In the transplantation field, the use of ANNs has been considered in the prediction of fibrosis in hepatitis C virus infected liver transplant recipients (12), prediction of outcomes after liver transplantation (21), and prediction of graft failure (22).…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Approach to Predict Acute Allograft Rejection in Liver Transplant Recipients Using Routine Laboratory Data

Zare

Zarei

et al. 2017

Hepat Mon

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Background: Discovery of non-invasive methods for acute rejection in liver transplant patients would contribute to preservation of liver function in the graft. Recently, however, outcome prediction based on biostatistical models like artificial neural networks (ANNs) is increasingly becoming impressive in medicine. Objectives: The aim of this study was to obtain a predictive model based on ANN technique and to figure out the best time for early prediction of acute allograft rejection after transplantation in liver transplant recipients. Methods: Feed-forward, back-propagation neural network was developed to predict acute rejection in liver transplant recipients using clinical and biochemical data from 148 liver transplant recipients over days 3, 7, and 14 post-transplantation. Sensitivity and receiver-operating characteristic (ROC) analysis were done to reveal the importance of input variables and the performance of the neural network. Results:The results were compared with a logistic regression (LR) model using the same data. Our results showed that the data related to day 7 gave the best results in terms of ANN performance; and the most important factors in the predictive model were aspartate aminotransferase (AST) and alanine aminotransferase (ALT). The ANN's accuracy was 90%, sensitivity was 87%, specificity was 90% in the testing set, and the performance of the ANN was better than that of the LR model. The ANN recognized correctly eight out of ten acute rejection patients and 34 out of 36 non-rejection ones in the testing set. Conclusions: This study suggests that ANN could be a valuable adjunct to conventional liver function tests for monitoring liver transplant recipients in the early postoperative period.

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A neural network approach to predicting outcomes in heart failure using cardiopulmonary exercise testing

Cited by 44 publications

References 37 publications

A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty

A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty

An intelligent recommender system based on predictive analysis in telehealthcare environment

A Neural Network Approach to Predict Acute Allograft Rejection in Liver Transplant Recipients Using Routine Laboratory Data

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