Use of an artificial neural network to predict persistent organ failure in patients with acute pancreatitis

Hong, Wandong; Chen, Xiangrong; Jin, Shuqing; Huang, Qinying; Zhu, Qi-huai; Pan, Jingye

doi:10.6061/clinics/2013(01)rc01

Cited by 34 publications

(30 citation statements)

References 18 publications

(26 reference statements)

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“…Their model predicted a severe attack with an AUROC curve of 0.82, 87% sensitivity, and 71% specificity. Hong et al 141 created an ANN to evaluate patients with acute pancreatitis based on their age, hematocrit, serum levels of glucose and calcium, and blood level of urea nitrogen-this model identified patients with persistent organ failure with 96.2% accuracy. Jovanovic et al 142 developed an ANN model to identify patients with suspected choledocholithiasis who require therapeutic endoscopic retrograde cholangiopancreatography based on clinical, laboratory, and transcutaneous ultrasound findings; it did so with an AUROC curve of 0.88.…”

Section: Liver and Pancreatobiliary Disordersmentioning

confidence: 99%

Application of Artificial Intelligence to Gastroenterology and Hepatology

et al. 2020

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Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

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Section: Liver and Pancreatobiliary Disordersmentioning

confidence: 99%

Application of Artificial Intelligence to Gastroenterology and Hepatology

et al. 2020

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“…Apart from these, MLP is seen to be used in many other areas. Some of these examples are; developing neural networks to investigate relationships between air quality and quality of life indicators [44], applying artificial neural network algorithms to estimate suspended sediment load (case study: Kasilian catchment, Iran) [45], importance of artificial neural network in medical diagnosis disease like acute nephritis disease and heart disease [46], acomparative study on breast cancer prediction using RBF and MLP [47], comparative analysis of multilayer perceptron and radial basis function ANN for prediction of cycle time of structural subassembly manufacturing [48], comparison of artificial neural network, logistic regression and discriminant analysis efficiency in determining risk factors of type 2 diabetes [49], neural networks and multivariate statistical methods in traffic accident modelling [50],application of ANN to assess credit risk: a predictive model for credit card scoring [51], use of an artificial neural network to predict persistent organ failure in patients with acute pancreatitis [52], behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers [53] and predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models [54].…”

Section: The Determination Of the Criteria Weightsmentioning

confidence: 99%

A New Multi Criteria Decision Making Method : Approach of Logarithmic Concept (APLOCO)

Bulut¹

2018

IJAIA

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The primary aim of the study is to introduce APLOCO method which is developed for the solution of multicriteria decision making problems both theoretically and practically. In this context, application subject of APLACO constitutes evaluation of investment potential of different cities in metropolitan status in Turkey. Manisa, Denizli, Izmir, Kocaeli, Bursa, Ankara, Adana, and Antalya, respectively. Although APLOCO is used to solve the ranking problem in order to show application process in the paper, it can be employed easily in the solution of classification and selection problems. On the other hand, the study also shows a rare example of the nested usage of APLOCO which is one of the methods of operation research as well as MLP used in determination of weights. The secondary purpose of the study is to identify the independent variables affecting the factories in the operating phase and to estimate the effect levels of independent variables on the dependent variable in the organized industrial zones (OIZs), whose mission is to reduce regional development disparities and to mobilize local production dynamics. For this purpose, the effect levels of independent variables on dependent variables have been determined using the multilayer perceptron (MLP) method, which has a wide use in artificial neural networks (ANNs). The effect levels derived from MLP have been then used as the weight levels of the decision criteria in APLOCO. The independent variables included in MLP are also used as the decision criteria in APLOCO. According to the results obtained from APLOCO, Istanbul city is the best alternative in term of the investment potential and other alternatives are KEYWORDS approach of logarithmic concept (APLOCO), multi criteria decision analysis (MCDA), multilayer perceptron (MLP), organized industrial zones (OIZs), artificial intelligence, artifical neural networks (ANNs)

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“…Nowadays, there is an increasing body of research applying ANN analysis to clinical diagnosis, since it allows to establish complex interactions among variables involved in some multifactorial pathologies [20]. In fact, recent studies have provided satisfactory diagnostic results in different clinical areas [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis

Peña-Bautista

Oger

Baquero

et al. 2019

Clinical Biochemistry

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A B S T R A C TObjective: Lipid peroxidation constitutes a molecular mechanism involved in early Alzheimer Disease (AD) stages, and artificial neural network (ANN) analysis is a promising non-linear regression model, characterized by its high flexibility and utility in clinical diagnosis. ANN simulates neuron learning procedures and it could provide good diagnostic performances in this complex and heterogeneous disease compared with linear regression analysis. Design and Methods: In our study, a new set of lipid peroxidation compounds were determined in urine and plasma samples from patients diagnosed with early Alzheimer Disease (n = 70) and healthy controls (n = 26) by means of ultra-performance liquid chromatography coupled with tandem mass-spectrometry. Then, a model based on ANN was developed to classify groups of participants. Results: The diagnostic performances obtained using an ANN model for each biological matrix were compared with the corresponding linear regression model based on partial least squares (PLS), and with the non-linear (radial and polynomial) support vector machine (SVM) models. Better accuracy, in terms of receiver operating characteristic-area under curve (ROC-AUC), was obtained for the ANN models (ROC-AUC 0.882 in plasma and 0.839 in urine) than for PLS and SVM models. Conclusion: Lipid peroxidation and ANN constitute a useful approach to establish a reliable diagnosis when the prognosis is complex, multidimensional and non-linear. [17]. Neurons are placed in several layers (input, hidden, output) in the https://doi.

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Use of an artificial neural network to predict persistent organ failure in patients with acute pancreatitis

Cited by 34 publications

References 18 publications

Application of Artificial Intelligence to Gastroenterology and Hepatology

Application of Artificial Intelligence to Gastroenterology and Hepatology

A New Multi Criteria Decision Making Method : Approach of Logarithmic Concept (APLOCO)

Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis

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