Application of an Artificial Neural Network to Predict Postinduction Hypotension During General Anesthesia

Lin, Chao-Shun; Chang, Chia‐Yi; Chiu, Jainn Shiun; Lee, Yuan-Wen; Lin, Jui-An; Mok, Martin S.; Chiu, Hung-Wen; Li, Yu-Chuan

doi:10.1177/0272989x10379648

Cited by 32 publications

(18 citation statements)

References 29 publications

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“…However, it is difficult to evaluate an individual’s risk of dyslipidemia when many predictors exist concomitantly. A model that integrates related factors and predicts the risk of dyslipidemia would be helpful to promote health education and counseling, and enable further development of computerized medical decision support systems for aiding healthcare practitioners to assess the risks of their patients quickly, inexpensively, and noninvasively [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents

Wang

et al. 2012

PLoS ONE

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BackgroundDyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population.MethodsDemographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35–78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve.ResultsSome risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, P<0.001).ConclusionThe ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data.

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Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents

Wang

et al. 2012

PLoS ONE

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“…Neural networks are more and more widely used in medical sciences [ 37 , 42 , 45 , 54 , 60 ]. In cardiology, they are used, inter alia, to assess the status of cardiovascular system [ 43 ], to predict the risk of coronary heart disease [ 35 ] in ECG analysis [ 36 , 56 ] or echocardiography [ 59 ].…”

Section: Discussionmentioning

confidence: 99%

Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients

Kusy

Obrzut

Kluska

2013

Med Biol Eng Comput

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The aim of this article was to compare gene expression programming (GEP) method with three types of neural networks in the prediction of adverse events of radical hysterectomy in cervical cancer patients. Onehundred and seven patients treated by radical hysterectomy were analyzed. Each record representing a single patient consisted of 10 parameters. The occurrence and lack of perioperative complications imposed a two-class classification problem. In the simulations, GEP algorithm was compared to a multilayer perceptron (MLP), a radial basis function network neural, and a probabilistic neural network. The generalization ability of the models was assessed on the basis of their accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve (AUROC). The GEP classifier provided best results in the prediction of the adverse events with the accuracy of 71.96 %. Comparable but slightly worse outcomes were obtained using MLP, i.e., 71.87 %. For each of measured indices: accuracy, sensitivity, specificity, and the AUROC, the standard deviation was the smallest for the models generated by GEP classifier.

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“…Modeling results were expressed as the odds ratios (OR) with 95% confidence intervals (95% CI). The Hosmer-Lemeshow test ( H ) was used to assess the fit of the models, which divides subjects into deciles based on their predicted probabilities before computing Chi-square values from the observed and expected frequencies [17-20]. …”

Section: Methodsmentioning

confidence: 99%

A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis

Son

Jang

Seo

et al. 2012

BMC Med Inform Decis Mak

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BackgroundThe aim of this study is to develop a simple and reliable hybrid decision support model by combining statistical analysis and decision tree algorithms to ensure high accuracy of early diagnosis in patients with suspected acute appendicitis and to identify useful decision rules.MethodsWe enrolled 326 patients who attended an emergency medical center complaining mainly of acute abdominal pain. Statistical analysis approaches were used as a feature selection process in the design of decision support models, including the Chi-square test, Fisher's exact test, the Mann-Whitney U-test (p < 0.01), and Wald forward logistic regression (entry and removal criteria of 0.01 and 0.05, or 0.05 and 0.10, respectively). The final decision support models were constructed using the C5.0 decision tree algorithm of Clementine 12.0 after pre-processing.ResultsOf 55 variables, two subsets were found to be indispensable for early diagnostic knowledge discovery in acute appendicitis. The two subsets were as follows: (1) lymphocytes, urine glucose, total bilirubin, total amylase, chloride, red blood cell, neutrophils, eosinophils, white blood cell, complaints, basophils, glucose, monocytes, activated partial thromboplastin time, urine ketone, and direct bilirubin in the univariate analysis-based model; and (2) neutrophils, complaints, total bilirubin, urine glucose, and lipase in the multivariate analysis-based model. The experimental results showed that the model with univariate analysis (80.2%, 82.4%, 78.3%, 76.8%, 83.5%, and 80.3%) outperformed models using multivariate analysis (71.6%, 69.3%, 73.7%, 69.7%, 73.3%, and 71.5% with entry and removal criteria of 0.01 and 0.05; 73.5%, 66.0%, 80.0%, 74.3%, 72.9%, and 73.0% with entry and removal criteria of 0.05 and 0.10) in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under ROC curve, during a 10-fold cross validation. A statistically significant difference was detected in the pairwise comparison of ROC curves (p < 0.01, 95% CI, 3.13-14.5; p < 0.05, 95% CI, 1.54-13.1). The larger induced decision model was more effective for identifying acute appendicitis in patients with acute abdominal pain, whereas the smaller induced decision tree was less accurate with the test data.ConclusionsThe decision model developed in this study can be applied as an aid in the initial decision making of clinicians to increase vigilance in cases of suspected acute appendicitis.

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Application of an Artificial Neural Network to Predict Postinduction Hypotension During General Anesthesia

Abstract: The ANN model developed in this study had good discrimination and calibration and would provide decision support to clinicians and increase vigilance for patients at high risk of postinduction hypotension during general anesthesia.

Cited by 32 publications

References 29 publications

Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents

Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents

Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients

A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis

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