Use of an Artificial Neural Network to Predict Risk Factors of Nosocomial Infection in Lung Cancer Patients

Chen, Jie; Pan, Qinshi; Hong, Wandong; Pan, Jingye; Zhang, Wenhui; Xu, Gang; Wang, Yumin

doi:10.7314/apjcp.2014.15.13.5349

Cited by 13 publications

(13 citation statements)

References 12 publications

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“…Based on the univariate analysis, the risk prediction model using BP-ANN involving 9 significant factors for predicting HAI occurrence among elderly PD patients was finally constructed. The overall prediction accuracy of the model, 93.79%, was obviously higher than the reported predictive rate of 75.7% conducted in lung cancer patients [32] and 82.49% conducted among hematological diseases patients [41], and also higher than the reported predictive performance conducted by 10-point Braga risk score among patients underwent PD [28]. It had indicated that BP neural network prediction accuracy could reach approximately 90% by scientifically determining risk indicators and constructing appropriate network models [42], which was well verified in this study.…”

Section: Discussioncontrasting

confidence: 68%

“…It had indicated that BP neural network prediction accuracy could reach approximately 90% by scientifically determining risk indicators and constructing appropriate network models [42], which was well verified in this study. The sensitivity of the risk prediction model in this study was 0.67 while the specificity was 0.97, which were also both higher than other similar studies [32,41]. Furthermore, the AUC value of 0.94 was higher than the study among lung cancer patients conducted by Chen et al [32].…”

Section: Discussioncontrasting

confidence: 59%

“…A systematic review pointed out that artificial neural network (ANN) is undoubtedly more advantageous than traditional statistical methods in predicting clinical outcomes when the relationship between outcome-related variables is complex, multi-dimensional and non-linear [34]. And its practicality has been verified among lung cancer patients, blood disease patients, and ICU patients [32,[35][36]. As one of the most common artificial neural networks, the back propagation artificial neural network (BP-ANN), based on the simulation of biological neural network, has also been proven to acquire an excellent performance in predictive model construction, of which prominent advantage is that it is unnecessary to determine the mathematical equation of the mapping relationship between input and output variables in advance, with the actual output results gradually approaching to the expected output value by error propagation [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…As one of the most common artificial neural networks, the back propagation artificial neural network (BP-ANN), based on the simulation of biological neural network, has also been proven to acquire an excellent performance in predictive model construction, of which prominent advantage is that it is unnecessary to determine the mathematical equation of the mapping relationship between input and output variables in advance, with the actual output results gradually approaching to the expected output value by error propagation [29][30][31]. Nowadays, BP-ANN has been widely applied into various complex systems and model constructions that contain multiple and non-linear or unclear relationship variables [32][33], which may be also suitable for predicting the HAIs underpinned by complex factors among patients after PD. Consequently, this study aims to construct a risk prediction model for HAI occurrence among elderly patients after PD using BP-ANN model, and to verify its predictive performance.…”

Section: Introductionmentioning

confidence: 99%

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The Risk Prediction Model for Healthcare-Associated Infections among Elderly Patients Underwent Pancreaticoduodenectomy

Feng

Yao

et al. 2021

Preprint

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Background: The elderly are a high-risk group of healthcare-associated infections (HAIs) after pancreaticoduodenectomy (PD), and the effective prediction model may be beneficial to HAIs control. Methods: The data were obtained from Hospital Infection Surveillance System. The BP-ANN model was conducted according to univariate analysis. The receiver operating characteristic curve (ROC), the prediction accuracy, sensitivity and specificity were used to estimate the predicted performance. The final weight coefficients were calculated to illustrate the relative importance of indicators. Results: Of 688 elderly patients underwent PD, 83 (12.06%) were diagnosed with HAIs. 9 significant factors (P<0.05) including weight, fever, continuous fever for more than three days, blood routine abnormal percentage, ever livered in intensive care unit (ICU), antibacterial combination, postoperative antibacterial use days, ventilator use and urinary catheter use days were included into prediction model. The prediction accuracy in testing sets was 93.79%, and the sensitivity and specificity were 0.67 and 0.97. The contribution level of 9 significant factors were 10.65%, 8.54%, 10.17%, 9.64%, 9.26%, 10.02%, 12.53%, 11.90% and 17.29%, respectively. Conclusions: The 9-9-1 BP-ANN prediction model underpinned by complex factors in this study has relatively excellent performance for HAIs prediction among elderly patients after PD. Urinary catheter use days, postoperative antibacterial use days, ventilator use, weight and continuous fever for more than three days are the top five contribution indicators for HAIs prediction, which should be fully taken into consideration when developing HAIs prediction for the PD patients.

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Section: Discussioncontrasting

confidence: 68%

Section: Discussioncontrasting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Risk Prediction Model for Healthcare-Associated Infections among Elderly Patients Underwent Pancreaticoduodenectomy

Feng

Yao

et al. 2021

Preprint

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“…The correlations between serological biomarkers and the reverse of liver biopsy score are nonlinear and complex. Several research studies have explored the artificial neural network (ANN) model to estimate the correlation between serological biomarkers and the reverse of liver cirrhosis [7, 8]. ANNs base on the machine learning mechanism to identify the complex relationship between input neural units and output neural units.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients

Wei

Zhou

et al. 2019

Computational and Mathematical Methods in Medicine

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The diagnostic performance of an artificial neural network model for chronic HBV-induced liver fibrosis reverse is not well established. Our research aims to construct an ANN model for estimating noninvasive predictors of fibrosis reverse in chronic HBV patients after regular antiviral therapy. In our study, 141 consecutive patients requiring liver biopsy at baseline and 1.5 years were enrolled. Several serum biomarkers and liver stiffness were measured during antiviral therapy in both reverse and nonreverse groups. Statistically significant variables between two groups were selected to form an input layer of the ANN model. The ROC (receiver-operating characteristic) curve and AUC (area under the curve) were calculated for comparison of effectiveness of the ANN model and logistic regression model in predicting HBV-induced liver fibrosis reverse. The prevalence of fibrosis reverse of HBV patients was about 39% (55/141) after 78-week antiviral therapy. The Ishak scoring system was used to assess fibrosis reverse. Our study manifested that AST (aspartate aminotransferase; importance coefficient = 0.296), PLT (platelet count; IC = 0.159), WBC (white blood cell; IC = 0.142), CHE (cholinesterase; IC = 0.128), LSM (liver stiffness measurement; IC = 0.125), ALT (alanine aminotransferase; IC = 0.110), and gender (IC = 0.041) were the most crucial predictors of reverse. The AUC of the ANN model and logistic model was 0.809 ± 0.062 and 0.756 ± 0.059, respectively. In our study, we concluded that the ANN model with variables consisting of AST, PLT, WBC, CHE, LSM, ALT, and gender may be useful in diagnosing liver fibrosis reverse for chronic HBV-induced liver fibrosis patients.

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Systems Biology and Hospital-Associated Infections

Ghosh,

Lahiri,

Nag

et al. 2024

Systems Biology Approaches: Prevention, Diagnosis, and Understanding Mechanisms of Complex Diseases

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Use of an Artificial Neural Network to Predict Risk Factors of Nosocomial Infection in Lung Cancer Patients

Cited by 13 publications

References 12 publications

The Risk Prediction Model for Healthcare-Associated Infections among Elderly Patients Underwent Pancreaticoduodenectomy

The Risk Prediction Model for Healthcare-Associated Infections among Elderly Patients Underwent Pancreaticoduodenectomy

Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients

Systems Biology and Hospital-Associated Infections

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