Artificial Neural Network Model for Predicting 5-Year Mortality After Surgery for Hepatocellular Carcinoma: A Nationwide Study

Shi, Hon-Yi; Lee, King‐Teh; Wang, Jhi-Joung; Sun, Ding; Lee, Hao-Hsien; Chiu, Chong‐Chi

doi:10.1007/s11605-012-1986-3

Cited by 33 publications

(27 citation statements)

References 18 publications

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“…This study has demonstrated that these limitations may be overcome with the application of MLPNN. MLPNN has been used as successful predictive tools in other aspects of surgery and has been adapted to assess varying problems, such as the diagnosis of appendicitis, and predicting survival times in cancer patients [9][10][11][12]. This study reports on the application of ANNs as a simple and intelligent system to predict the clinical outcomes of ERAS.…”

Section: Discussionmentioning

confidence: 99%

“…Often, a relatively sophisticated understanding of statistics is required in order to implement derivative models at the clinical level. Therefore, newer and simpler techniques, such as artificial neural networks (ANNs), have been developed and have already been applied in other clinical fields to predict outcomes [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…This is because ANNs are able to capture many kinds of relationships and interactions between multiple inputting factors, and the solution is not restricted to a linear form, unlike in conventional statistics. ANNs have now been used in both the diagnosis of disease and prediction of outcomes with encouraging results in the fields of cancer diagnosis and survival, and the diagnosis of appendicitis [7][8][9][10][11][12][13][14][15][16]. However, the use of ANNs has not been reported within the field of ERAS.…”

Section: Introductionmentioning

confidence: 99%

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The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery

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“…The MLP network is an emerging tool for designing special classes of layered feed-forward networks. [13][14][15] Its input layer consists of source nodes, and its output layer consists of neurons; both layers connect the network to the outside world. The RBF network, another popular layered feed-forward network, uses memory-based learning.…”

Section: Development Of the Ann Modelmentioning

confidence: 99%

“…Specifically, learning is considered a curve-fitting problem in highdimensional space. [13][14][15] The RBF and MLP models in this study used the same method of determining when to stop the training sessions. A nonlinear sigmoid function was used as a transfer function for each neuron in the hidden and output layers of the networks.…”

Section: Development Of the Ann Modelmentioning

confidence: 99%

In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

Chen

Shi

Lee

et al. 2013

American Journal of Critical Care

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Background Few studies have used pooled data for more than 2 years and few have analyzed data for patients receiving mechanical ventilation in Taiwan. Objective To validate the use of an artificial neural network model for predicting in-hospital mortality in patients receiving mechanical ventilation in Taiwan and to compare the predictive accuracy of the artificial neural network model with that of a logistic regression model. Methods Retrospective comparison of 1000 pairs of data sets processed by logistic regression and artificial neural network models based on initial clinical data for 213 945 patients receiving mechanical ventilation. For each pair of artificial neural network and logistic regression models, the area under the receiver operating characteristic curves, Hosmer-Lemeshow statistics, and accuracy rate were calculated and compared by using t tests. Global sensitivity analysis and sensitivity score approach were also used to assess the relative significance of input parameters in the system model and the relative importance of variables. Results Compared with the logistic regression model, the artificial neural network model had a better accuracy rate in 96.3% of cases, better Hosmer-Lemeshow statistics in 41.2% of cases, and a better area under the curve in 97.6% of cases. Hospital volume was the most influential (sensitive) variable affecting in-hospital mortality, followed by Charlson comorbidity index, length of stay, and hospital type. Conclusions Compared with the conventional logistic regression model, the artificial neural network model was more accurate in predicting in-hospital mortality and had higher overall performance indices.

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Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease

Søreide

Thorsen

Søreide

2014

Eur J Trauma Emerg Surg

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PurposeMortality prediction models for patients with perforated peptic ulcer (PPU) have not yielded consistent or highly accurate results. Given the complex nature of this disease, which has many non-linear associations with outcomes, we explored artificial neural networks (ANNs) to predict the complex interactions between the risk factors of PPU and death among patients with this condition.MethodsANN modelling using a standard feed-forward, back-propagation neural network with three layers (i.e., an input layer, a hidden layer and an output layer) was used to predict the 30-day mortality of consecutive patients from a population-based cohort undergoing surgery for PPU. A receiver-operating characteristic (ROC) analysis was used to assess model accuracy.ResultsOf the 172 patients, 168 had their data included in the model; the data of 117 (70 %) were used for the training set, and the data of 51 (39 %) were used for the test set. The accuracy, as evaluated by area under the ROC curve (AUC), was best for an inclusive, multifactorial ANN model (AUC 0.90, 95 % CIs 0.85–0.95; p < 0.001). This model outperformed standard predictive scores, including Boey and PULP. The importance of each variable decreased as the number of factors included in the ANN model increased.ConclusionsThe prediction of death was most accurate when using an ANN model with several univariate influences on the outcome. This finding demonstrates that PPU is a highly complex disease for which clinical prognoses are likely difficult. The incorporation of computerised learning systems might enhance clinical judgments to improve decision making and outcome prediction.

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Artificial Neural Network Model for Predicting 5-Year Mortality After Surgery for Hepatocellular Carcinoma: A Nationwide Study

Cited by 33 publications

References 18 publications

The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery

The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery

In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease

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