Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients

Eggers, Kai M.; Ellenius, Johan; Dellborg, Mikael; Groth, Torgny; Oldgren, Jonas; Swahn, Eva; Lindahl, Bertil

doi:10.1016/j.ijcard.2005.12.019

Cited by 55 publications

(30 citation statements)

References 33 publications

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“…Chest pain patients were participants from the FASTER I (Fast Assessment of Thoracic Pain by Neural Networks) study that was conducted at 3 investigational centers in Sweden between October 2002 and August 2003 and enrolled 380 study participants (10 ). The sole criterion for inclusion of patients in the study was their admission to the coronary care unit because of chest pain lasting for Ն15 min within the previous 8 h. The study exclusion criteria were pathological ST-segment elevation on the admission 12-lead electrocardiogram or strong suspicion of acute myocarditis.…”

Section: Patientsmentioning

confidence: 99%

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Plasma Prolylcarboxypeptidase (Angiotensinase C) Is Increased in Obesity and Diabetes Mellitus and Related to Cardiovascular Dysfunction

Lind²,

Zhao

et al. 2012

Clinical Chemistry

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BACKGROUND: Prolylcarboxypeptidase (PRCP) (angiotensinase C) has 3 major targets, angiotensin II, prekallikrein, and ␣-melanocyte stimulating hormone [1][2][3][4][5][6][7][8][9][10][11][12][13] . The truncation of the latter leads to loss in appetite regulation and obesity in experimental animals. The objectives of this study were to purify PRCP from a native source, establish a sensitive immunoassay for PRCP, and relate plasma PRCP concentrations to signs and symptoms of obesity, diabetes mellitus, and cardiovascular dysfunction.

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Section: Patientsmentioning

confidence: 99%

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mentioning

confidence: 99%

Plasma Prolylcarboxypeptidase (Angiotensinase C) Is Increased in Obesity and Diabetes Mellitus and Related to Cardiovascular Dysfunction

Lind²,

Zhao

et al. 2012

Clinical Chemistry

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“…Many patients are discharged without investigation and one of the major benefits of this sort of decision aid would be to improve safety for early discharge. While cardiac markers would be useful for the model -indeed in [41] clinically useful, predictive values for diagnosis of AMI at two hours post admission were obtained using an ANN trained with serial measurements of either myoglobin or Troponin I -in clinical practice many patients do not have these measured. However, given the well-documented short-and long-term prognostic value of information derived from measurements of troponins and other proteins, future studies should examine how such measurements might be used alongside ECG and clinical data in developing a decision support system.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Automatic covariate selection in logistic models for chest pain diagnosis: A new approach

Harrison

Kennedy

2008

Computer Methods and Programs in Biomedicine

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A newly established method for optimizing logistic models via a minorizationmajorization procedure is applied to the problem of diagnosing acute coronary syndromes (ACS). The method provides a principled approach to the selection of covariates which would otherwise require the use of a suboptimal method owing to the size of the covariate set. A strategy for building models is proposed and two models optimized for performance and for simplicity are derived via ten-fold crossvalidation. These models confirm that a relatively small set of covariates including clinical and electrocardiographic features can be used successfully in this task.The performance of the models is comparable with previously published models using less principled selection methods. The models prove to be portable when tested on data gathered from three other sites. Whilst diagnostic accuracy and calibration diminishes slightly for these new settings, it remains satisfactory overall.The prospect of building predictive models that are as simple as possible for a required level of performance is valuable if data-driven decision aids are to gain wide acceptance in the clinical situation owing to the need to minimize the time taken to gather and enter data at the bedside.

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“…Different types of ANN have been used such as those based on radial basis functions [15]. Artificial intelligence classifiers have also been used in oncology and breast cancer diagnosis [10,16,17]; for pulmonary diseases [18]; in haematology [19] and cardiology [20,21,22,23]. Decision trees have been also widely used both to represent and to carry out making decision processes.…”

Section: Introductionmentioning

confidence: 99%

Aid decision algorithms to estimate the risk in congenital heart surgery

Ruiz-Fernández

Torra

Soriano-Payá

et al. 2016

Computer Methods and Programs in Biomedicine

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A c c e p t e d M a n u s c r i p t Highlights  We propose an alternative system for classifying the risk in paediatric congenital heart surgery. Four methods are tested: a perceptron multilayer, self-organising maps, a radial basis function neural network and decision trees. We obtain an accuracy of 99.87% (using pre and post-surgical data) and 83% (using just pre-surgical data).Page 2 In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. MethodsWe have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. ResultsAccuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. ConclusionsAccording to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.

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Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients

Cited by 55 publications

References 33 publications

Plasma Prolylcarboxypeptidase (Angiotensinase C) Is Increased in Obesity and Diabetes Mellitus and Related to Cardiovascular Dysfunction

Plasma Prolylcarboxypeptidase (Angiotensinase C) Is Increased in Obesity and Diabetes Mellitus and Related to Cardiovascular Dysfunction

Automatic covariate selection in logistic models for chest pain diagnosis: A new approach

Aid decision algorithms to estimate the risk in congenital heart surgery

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