Analysis of the applicability of artificial neural networks for studying blood plasma: determination of magnesium ion concentration as a case study

Liparini, Alexandre; Carvalho, Sandra Helena Vieira de; Belchior, Jadson C.

doi:10.1515/cclm.2005.161

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…ANNs are widely used in materials science, but they are also used in biological or medical studies (e.g., [33][34][35][36]). Only a few works can be found on the prediction of degradation behaviour of medical implants using an ANN [37][38].…”

Section: Introductionmentioning

confidence: 99%

Magnesium degradation as determined by artificial neural networks

2013

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Section: Introductionmentioning

confidence: 99%

Magnesium degradation as determined by artificial neural networks

2013

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“…A reason for selecting another training algorithm besides backpropagation, such as the radial basis function, is to overcome problems (Baxt, 1991;Baxt & Skora, 1996;Dorffner & Porenta, 1994;Lapuerta et al, 1995;Scott, Aziz, Yasuda & Gewirtz, 2004) Electromyography (Hassoun et al, 1994) Lung disease (Lin, Yan & Chen, 2005;Suzuki, Horiba, Sugie & Nanki, 2004) Pulmonary embolism (Evander et al, 2003;Fisher et al, 1996;Serpen et al, 2003) Tomography (Bruyndonckx et al, 2004;Tourassi & Floyd, 1995) Laboratory (produce test results) Breast disease (Mattfeldt, Kestler & Sinn, 2004) EEG (Güler, Übeyli & Güler, 2005b;Nowack, Walczak & Janati, 2002;Walczak & Nowack, 2001) General blood test pathology (Liparini, Carvalho & Belchior, 2005) Head injury (Erol et al, 2005) Heart disease (Andrisevic et al, 2005;Haraldsson et al, 2004;Mobley et al, 2005) Hematology (Zini, 2005), lung disease (Folland, Hines, Dutta, Boilot & Morgan, 2004;Güler, Polat & Ergün, 2005a) Resource Planning…”

Section: Neural Network Design Issuesmentioning

confidence: 99%

Nonparametric Decision Support Systems in Medical Diagnosis

Walczak

Brimhall

Lefkowitz

2006

International Journal of Healthcare Information Systems and Informatics

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Patients face a multitude of diseases, trauma, and related medical problems that are difficult and costly with respect to direct costs and diagnosis, including pulmonary embolism (PE), which has mortality rates as high as 10%. Advanced decision-making tools, such as nonparametric neural networks (NN), may improve diagnostic capabilities for these problematic medical conditions. The research develops a backpropagation trained neural network diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for PE, with almost 15% suffering a confirmed PE, were collected and used to evaluate various NN models' performances. Results indicate that using NN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, specifically the d-dimer assay and reactive glucose, significantly improving overall positive predictive value, compared to using either test in isolation, and also increasing negative predictive performance.

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“…The most representative construction of ANN consisted of three layers [25]. For clinicians and statisticians, the input layer represented the observed biomarkers of serum biochemical and auxiliary examinations [26,27]. The output layer was the indicator of clinical outcomes.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research

Wei

Yang

2021

Computational and Mathematical Methods in Medicine

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Introduction. A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure. Methods. We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria. Results. Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer. Conclusion. The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.

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Analysis of the applicability of artificial neural networks for studying blood plasma: determination of magnesium ion concentration as a case study

Cited by 7 publications

References 28 publications

Magnesium degradation as determined by artificial neural networks

Magnesium degradation as determined by artificial neural networks

Nonparametric Decision Support Systems in Medical Diagnosis

Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research

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