Applying an Artificial Neural Network to Predict Total Body Water in Hemodialysis Patients

Chiu, Jainn Shiun; Chong, Chee-Fah; Lin, Yuh Feng; Wu, Chia‐Chao; Wang, Yuh Feng; Li, Yu-Chuan

doi:10.1159/000088279

Cited by 37 publications

(32 citation statements)

References 57 publications

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“…Clinical studies have suggested the superiority of ANN, as compared to other statistical models, for the classification or prediction of morbidity [15,16]. The ability of the ANN to process more information in the context of multidimensionality of complex data to a large extent explains the superiority of ANN as a predicting model.…”

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confidence: 99%

Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks

Sy¹,

2008

Anaesthesia

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SummaryRisk-stratification models based on pre-operative patient and disease characteristics are useful for providing individual patients with an insight into the potential risk of complications and mortality, for aiding the clinical decision for surgery vs non-surgical therapy, and for comparing the quality of care between different surgeons or hospitals. Our study aimed to apply artificial neural networks (ANN) models to predict mortality and morbidity after cardiac surgery, and also to compare the efficacy of this model to that of the logistic regression model and Parsonnet score. The accuracy of the ANN, logistic regression and Parsonnet score in predicting mortality was 83.8%, 87.9% and 78.4%. The accuracy of the ANN, logistic regression and Parsonnet score in predicting major morbidity was 79.0%, 74.3% and 68.6%. The area under the receiver operating characteristic curves (AUC) of the ANN, logistic regression and Parsonnet score in predicting in-hospital mortality were 0.873, 0.852 and 0.829. The AUCs of the ANN, logistic regression and Parsonnet score in predicting major morbidity were 0.852, 0.789 and 0.727. The results showed the ANN models have the best discriminating power in predicting in-hospital mortality and morbidity among these models. Advances in medical treatment, the advent of thrombolytic therapy, and the availability of a range of percutaneous angiographic interventions has changed the profile of patients referred for cardiac surgery [1,2]. Referred patients tend to be older, with a substantial increase in the proportion of high-risk patients [3][4][5]. Risk-stratification models based on pre-operative patient and disease characteristics are useful for providing patients with an insight into the potential risk of complications and mortality, for aiding the clinical decision for surgery vs non-surgical therapy, and for comparing the quality of care between different surgeons and hospitals.Parsonnet and colleagues first designed a risk stratification model for evaluation of the mortality after cardiac surgery in 1989 [6]. The Parsonnet score is a simple scoring system which derived from large international statistics tries, on the basis of pre-operative risk factors, to assess and predict the mortality of patients with coronary and heart valve operations. Thereafter, numerous multifactorial risk scores have been developed to predict the outcomes following cardiac surgery [7][8][9][10][11][12]. Most of the scoring systems have been designed to predict mortality, but postoperative morbidity is recognised as a major determinant of hospital costs and quality of life after surgery [13]. Relatively few studies have involved the prediction of postoperative morbidity following cardiac surgery [8,14].Logistic regression models the probability of some event occurring as a linear function of a set of predictor variables. The actual state of the dependent variable is determined by looking at the estimated probability.The artificial neural network (ANN) is a computational model with parallel non-linear pr...

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confidence: 99%

Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks

Sy¹,

2008

Anaesthesia

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“…However, we selected only six variables that are usually measured in hemodialysis patients. Our reason is that for any forecasting model to be useful in making clinical decisions, it should use only parameters that are readily available to the clinician at the time of triage (Chiu et al 2005). This is an essential issue since fewer inputs may simplify the process for clinicians in determining subsequent decisions rapidly (Bates et al 2003).…”

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confidence: 99%

Neuro-Fuzzy Technology as a Predictor of Parathyroid Hormone Level in Hemodialysis Patients

Chen

Li²,

Lin

et al. 2007

Tohoku J. Exp. Med.

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“…An artificial neural network (ANN) is a connectionist model composed of non-linear computational elements called 'neurons or axons' arranged in highly interconnected layers with a structure that simulates the transfer of information through the nervous system (8). By changing the transfer functions and the associated parameters, this constructed neural network adapts itself to the pattern of the input variables and eventually generates numbers that iteratively solves to values of the designated output variables (9).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Drug Dialyzability Prediction

Daheb¹,

Lipman²,

Hildgen³

et al. 2013

J Pharm Pharm Sci

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-Purpose. The purpose of this study was to develop an artificial neural network (ANN) model to predict drug removal during dialysis based on drug properties and dialysis conditions. Nine antihypertensive drugs were chosen as model for this study. Methods. Drugs were dissolved in a physiologic buffer and dialysed in vitro in different dialysis conditions (UFRmin/UFRmax, with/without BSA). Samples were taken at regular intervals and frozen at -20ºC until analysis. Extraction methods were developed for drugs that were dialysed with BSA in the buffer. Drug concentrations were quantified by high performance liquid chromatography (HPLC) or mass spectrometry (LC/MS/MS). Dialysis clearances (CLDs) were calculated using the obtained drug concentrations. An ANOVA with Scheffe's pairwise adjustments was performed on the collected data in order to investigate the impact of drug plasma protein binding and ultrafiltration rate (UFR) on CLD. The software Neurosolutions ® was used to build ANNs that would be able to predict drug CLD (output). The inputs consisted of dialysis UFR and the herein drug properties: molecular weight (MW), logD and plasma protein binding. Results. Observed CLDs were very high for the majority of the drugs studied. The addition of BSA in the physiologic buffer statistically significantly decreased CLD for carvedilol (p= 0.002) and labetalol (p<0.001), but made no significant difference for atenolol (p= 0.100). In contrast, varying UFR does not significantly affect CLD (p>0.025). Multiple ANNs were built and compared, the best model was a Jordan and Elman network which showed learning stability and good predictive results (MSE testing = 129). Conclusion. In this study, we have developed an ANN-model which is able to predict drug removal during dialysis. Since experimental determination of all existing drug CLDs is not realistic, ANNs represent a promising tool for the prediction of drug CLD using drug properties and dialysis conditions.

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Applying an Artificial Neural Network to Predict Total Body Water in Hemodialysis Patients

Cited by 37 publications

References 57 publications

Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks

Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks

Neuro-Fuzzy Technology as a Predictor of Parathyroid Hormone Level in Hemodialysis Patients

Artificial Neural Network Modeling for Drug Dialyzability Prediction

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