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Yasnitsky LN, Dumler AA, Cherepanov FM, Yasnitsky VL, Uteva NA. Capabilities of neural network technologies for extracting new medical knowledge and enhancing precise decision making for patients. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2021. [DOI: 10.1080/23808993.2021.1993595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Leonid N. Yasnitsky
- Department of Applied Mathematics and Informatics, Perm State University, Perm, Russia
- Department of Information Technology in Business, Higher School of Economics, Perm, Russia
| | - Andrey A. Dumler
- Department of Propedeutics of Internal Diseases No. 1, Perm State Medical University, Perm, Russia
| | - Fedor M. Cherepanov
- Department of Applied Informatics, Perm State University for Humanities and Pedagogy, Perm, Russia
| | - Vitaly L. Yasnitsky
- Department of Computational Mathematics, Mechanics and Biomechanics, Perm National Research Polytechnic University, Perm, Russia
| | - Natalia A. Uteva
- Department of Propedeutics of Internal Diseases No. 1, Perm State Medical University, Perm, Russia
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Liu W, Liu X, Peng M, Chen GQ, Liu PH, Cui XW, Jiang F, Dietrich CF. Artificial intelligence for hepatitis evaluation. World J Gastroenterol 2021; 27:5715-5726. [PMID: 34629796 PMCID: PMC8473592 DOI: 10.3748/wjg.v27.i34.5715] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/28/2021] [Accepted: 08/02/2021] [Indexed: 02/06/2023] Open
Abstract
Recently, increasing attention has been paid to the application of artificial intelligence (AI) to the diagnosis of diverse hepatic diseases, which comprises traditional machine learning and deep learning. Recent studies have shown the possible value of AI based data mining in predicting the incidence of hepatitis, classifying the different stages of hepatitis, diagnosing or screening for hepatitis, forecasting the progression of hepatitis, and predicting response to antiviral drugs in chronic hepatitis C patients. More importantly, AI based on radiology has been proven to be useful in predicting hepatitis and liver fibrosis as well as grading hepatocellular carcinoma (HCC) and differentiating it from benign liver tumors. It can predict the risk of vascular invasion of HCC, the risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis, and the risk of liver failure after hepatectomy in HCC patients. In this review, we summarize the application of AI in hepatitis, and identify the challenges and future perspectives.
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Affiliation(s)
- Wei Liu
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Xue Liu
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Mei Peng
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi 445000, Hubei Province, China
| | - Peng-Hua Liu
- Department of Medical Ultrasound, The First Affiliated Hospital of Shaoyang University, Shaoyang 422000, Hunan Province, China
| | - Xin-Wu Cui
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Fan Jiang
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern 3626, Switzerland
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Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6662779. [PMID: 33727951 PMCID: PMC7937476 DOI: 10.1155/2021/6662779] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 12/10/2020] [Accepted: 02/18/2021] [Indexed: 02/08/2023]
Abstract
Introduction A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure. Methods We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria. Results Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer. Conclusion The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.
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Liu W, Bao C, Zhou Y, Ji H, Wu Y, Shi Y, Shen W, Bao J, Li J, Hu J, Huo X. Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China. BMC Infect Dis 2019; 19:828. [PMID: 31590636 PMCID: PMC6781406 DOI: 10.1186/s12879-019-4457-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 09/10/2019] [Indexed: 08/22/2023] Open
Abstract
Background Hand, foot and mouth disease (HFMD) is a rising public health problem and has attracted considerable attention worldwide. The purpose of this study was to develop an optimal model with meteorological factors to predict the epidemic of HFMD. Methods Two types of methods, back propagation neural networks (BP) and auto-regressive integrated moving average (ARIMA), were employed to develop forecasting models, based on the monthly HFMD incidences and meteorological factors during 2009–2016 in Jiangsu province, China. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were employed to select model and evaluate the performance of the models. Results Four models were constructed. The multivariate BP model was constructed using the HFMD incidences lagged from 1 to 4 months, mean temperature, rainfall and their one order lagged terms as inputs. The other BP model was fitted just using the lagged HFMD incidences as inputs. The univariate ARIMA model was specified as ARIMA (1,0,1)(1,1,0)12 (AIC = 1132.12, BIC = 1440.43). And the multivariate ARIMAX with one order lagged temperature as external predictor was fitted based on this ARIMA model (AIC = 1132.37, BIC = 1142.76). The multivariate BP model performed the best in both model fitting stage and prospective forecasting stage, with a MAPE no more than 20%. The performance of the multivariate ARIMAX model was similar to that of the univariate ARIMA model. Both performed much worse than the two BP models, with a high MAPE near to 40%. Conclusion The multivariate BP model effectively integrated the autocorrelation of the HFMD incidence series. Meanwhile, it also comprehensively combined the climatic variables and their hysteresis effects. The introduction of the climate terms significantly improved the prediction accuracy of the BP model. This model could be an ideal method to predict the epidemic level of HFMD, which is of great importance for the public health authorities.
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Affiliation(s)
- Wendong Liu
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China.
| | - Changjun Bao
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Yuping Zhou
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Hong Ji
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Ying Wu
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Yingying Shi
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Wenqi Shen
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Jing Bao
- Jiangsu Meteorological Service Center, Nanjing, China
| | - Juan Li
- Jiangsu Meteorological Service Center, Nanjing, China
| | - Jianli Hu
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Xiang Huo
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
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Manliura Datilo P, Ismail Z, Dare J. A Review of Epidemic Forecasting Using Artificial Neural Networks. INTERNATIONAL JOURNAL OF EPIDEMIOLOGIC RESEARCH 2019. [DOI: 10.15171/ijer.2019.24] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background and aims: Since accurate forecasts help inform decisions for preventive health-care intervention and epidemic control, this goal can only be achieved by making use of appropriate techniques and methodologies. As much as forecast precision is important, methods and model selection procedures are critical to forecast precision. This study aimed at providing an overview of the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It is necessary for forecasters to apply the right tools for the epidemic forecasts with high precision. Methods: It involved sampling and survey of epidemic forecasts based on ANN. A comparison of performance using ANN forecast and other methods was reviewed. Hybrids of a neural network with other classical methods or meta-heuristics that improved performance of epidemic forecasts were analysed. Results: Implementing hybrid ANN using data transformation techniques based on improved algorithms, combining forecast models, and using technological platforms enhance the learning and generalization of ANN in forecasting epidemics. Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, a working guide for the choice of appropriate tools will help reduce inconsistency and imprecision in forecasting epidemic size in populations. ANN hybrids that combined other algorithms and models, data transformation and technology should be used for an epidemic forecast.
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Affiliation(s)
- Philemon Manliura Datilo
- Department of Mathematical Sciences, Universiti Teknologi Malaysia, Johor, Malaysia
- Department of Information Technology, Modibbo Adama University of Technology, Yola School of Management and Information Technology, Adamawa State, Nigeria
| | - Zuhaimy Ismail
- Department of Mathematical Sciences, Universiti Teknologi Malaysia, Johor, Malaysia
| | - Jayeola Dare
- Adekunle Ajasin University, Department of Mathematical Sciences, Faculty of Science, Ondo State, Nigeria
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Wang Y, Xu C, Zhang S, Yang L, Wang Z, Zhu Y, Yuan J. Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China. Sci Rep 2019; 9:8046. [PMID: 31142826 PMCID: PMC6541597 DOI: 10.1038/s41598-019-44469-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 03/06/2019] [Indexed: 02/03/2023] Open
Abstract
The high incidence, seasonal pattern and frequent outbreaks of hand, foot, and mouth disease (HFMD) represent a threat for millions of children in mainland China. And advanced response is being used to address this. Here, we aimed to model time series with a long short-term memory (LSTM) based on the HFMD notified data from June 2008 to June 2018 and the ultimate performance was compared with the autoregressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NAR). The results indicated that the identified best-fitting LSTM with the better superiority, be it in modeling dataset or two robustness tests dataset, than the best-conducting NAR and seasonal ARIMA (SARIMA) methods in forecasting performances, including the minimum indices of root mean square error, mean absolute error and mean absolute percentage error. The epidemic trends of HFMD remained stable during the study period, but the reported cases were even at significantly high levels with a notable high-risk seasonality in summer, and the incident cases projected by the LSTM would still be fairly high with a slightly upward trend in the future. In this regard, the LSTM approach should be highlighted in forecasting the epidemics of HFMD, and therefore assisting decision makers in making efficient decisions derived from the early detection of the disease incidents.
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Affiliation(s)
- Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China
| | - Chunjie Xu
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, P.R. China
| | - Shengkui Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China
| | - Li Yang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China
| | - Zhende Wang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China
| | - Ying Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China
| | - Juxiang Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China.
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Wang YW, Shen ZZ, Jiang Y. Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China. PLoS One 2018; 13:e0201987. [PMID: 30180159 PMCID: PMC6122800 DOI: 10.1371/journal.pone.0201987] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 07/25/2018] [Indexed: 01/12/2023] Open
Abstract
Background Hepatitis B virus (HBV) infection is a major public health threat in China for China has a hepatitis B prevalence of more than one million people in 2017 year. Disease incidence prediction may help hepatitis B prevention and control. This study intends to build and compare 2 forecasting models for hepatitis B incidence in China. Methods Autoregressive integrated moving average (ARIMA) model and grey model GM(1,1) were adopted to fit the monthly incidence of hepatitis B in China from March 2010 to October 2017. The fitting and forecasting performances of the 2 models were evaluated. The better one was adopted to predict the incidence from November 2017 to March 2018. Database was built by Excel 2016 and statistical analysis was completed using R 3.4.3 software. Results Descriptive analysis showed that the incidence of hepatitis B in China has seasonal variation and has shown a downward trend from 2010 to 2017. We selected the ARIMA (3,1,1) (0,1,2)12 model among all the ARIMA models for it has the lowest AIC value. Model expression of GM (1,1) was X(1) (k + 1) = 3386876.7478e0.0249k − 3289206.7428. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA(3,1,1)(0,1,2)12 model were lower than GM(1,1) model on fitting part and forecasting part. According to the forecast results, the incidence may have a slight fluctuation during the following months. Conclusions The ARIMA model showed better hepatitis B fitting and forecasting performance than GM(1,1) model. It is a potential decision supportive tool for controlling hepatitis B in China before a predictive hepatitis B outbreak.
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Affiliation(s)
- Ya-wen Wang
- School of Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Zhong-zhou Shen
- School of Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yu Jiang
- School of Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- * E-mail:
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Yang X, Zou J, Kong D, Jiang G. The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China. Medicine (Baltimore) 2018; 97:e11787. [PMID: 30142765 PMCID: PMC6112867 DOI: 10.1097/md.0000000000011787] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Typhoid and paratyphoid fevers (TPF), systemic emerging infectious diseases, is a serious health problem for society. If the incidence trend of TPF can be predicted, prevention and control measures can be taken in advance to reduce the harm to the people's health.Grey Model First Order One Variable [GM (1, 1)] was applied to predict the incidence trend of TPF with the incidence data of TPF in Wuhan City of China from 2004 to 2015. The original data were acquired from the national surveillance system.The GM (1, 1) model was established as ŷ (t + 1) = 0.88 e + 0.15. The goodness-of-fit test indicated that the precision (degree 2) was qualified (C = 0.40, P = .91). We further compared actual values with predicted values in 2016 and found that GM (1, 1) model we built has excellent performance in incidence trend prediction.Our prediction shows that the TPF incidences in Wuhan City will be slowly decreasing in the next 3 years. It is, however, still necessary to strengthen the comprehensive prevention and control to reduce the incidence level of TPF.
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Affiliation(s)
| | - Jiaojiao Zou
- Wuhan Centers for Disease Prevention and Control
| | - Deguang Kong
- Wuhan Centers for Disease Prevention and Control
| | - Gaofeng Jiang
- Center for Translational Medicine, Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, Hubei, China
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Peng Y, Yu B, Wang P, Kong DG, Chen BH, Yang XB. Application of seasonal auto-regressive integrated moving average model in forecasting the incidence of hand-foot-mouth disease in Wuhan, China. Curr Med Sci 2017; 37:842-848. [PMID: 29270741 DOI: 10.1007/s11596-017-1815-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 04/11/2017] [Indexed: 12/30/2022]
Abstract
Outbreaks of hand-foot-mouth disease (HFMD) have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average (ARIMA) model for time series analysis was designed in this study. Eighty-four-month (from January 2009 to December 2015) retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination (R 2), normalized Bayesian Information Criterion (BIC) and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as (1,0,1)(0,1,1)12, with the largest coefficient of determination (R 2=0.743) and lowest normalized BIC (BIC=3.645) value. The residuals of the model also showed non-significant autocorrelations (P Box-Ljung (Q)=0.299). The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.
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Affiliation(s)
- Ying Peng
- Wuhan Centers for Disease Prevention and Control, Wuhan, 430015, China
| | - Bin Yu
- Wuhan Centers for Disease Prevention and Control, Wuhan, 430015, China
| | - Peng Wang
- Wuhan Centers for Disease Prevention and Control, Wuhan, 430015, China
| | - De-Guang Kong
- Wuhan Centers for Disease Prevention and Control, Wuhan, 430015, China
| | - Bang-Hua Chen
- Wuhan Centers for Disease Prevention and Control, Wuhan, 430015, China
| | - Xiao-Bing Yang
- Wuhan Centers for Disease Prevention and Control, Wuhan, 430015, China.
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Huang D, Wu Z. Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization. PLoS One 2017; 12:e0172539. [PMID: 28222194 PMCID: PMC5319685 DOI: 10.1371/journal.pone.0172539] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 02/05/2017] [Indexed: 11/18/2022] Open
Abstract
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.
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Affiliation(s)
- Daizheng Huang
- Department of Biomedical Engineering, School of Preclinical Medicine, Guangxi Medical University, Nanning, Guangxi Province, China
- * E-mail:
| | - Zhihui Wu
- Department of Biomedical Engineering, School of Preclinical Medicine, Guangxi Medical University, Nanning, Guangxi Province, China
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Zhang G, Dai M, Yang L, Li W, Li H, Xu C, Shi X, Dong X, Fu F. Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography. Biomed Eng Online 2017; 16:7. [PMID: 28086909 PMCID: PMC5234124 DOI: 10.1186/s12938-016-0294-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2016] [Accepted: 12/04/2016] [Indexed: 11/18/2022] Open
Abstract
Background Electrode disconnection is a common occurrence during long-term monitoring of brain electrical impedance tomography (EIT) in clinical settings. The data acquisition system suffers remarkable data loss which results in image reconstruction failure. The aim of this study was to: (1) detect disconnected electrodes and (2) account for invalid data. Methods Weighted correlation coefficient for each electrode was calculated based on the measurement differences between well-connected and disconnected electrodes. Disconnected electrodes were identified by filtering out abnormal coefficients with discrete wavelet transforms. Further, previously valid measurements were utilized to establish grey model. The invalid frames after electrode disconnection were substituted with the data estimated by grey model. The proposed approach was evaluated on resistor phantom and with eight patients in clinical settings. Results The proposed method was able to detect 1 or 2 disconnected electrodes with an accuracy of 100%; to detect 3 and 4 disconnected electrodes with accuracy of 92 and 84% respectively. The time cost of electrode detection was within 0.018 s. Further, the proposed method was capable to compensate at least 60 subsequent frames of data and restore the normal image reconstruction within 0.4 s and with a mean relative error smaller than 0.01%. Conclusions In this paper, we proposed a two-step approach to detect multiple disconnected electrodes and to compensate the invalid frames of data after disconnection. Our method is capable of detecting more disconnected electrodes with higher accuracy compared to methods proposed in previous studies. Further, our method provides estimations during the faulty measurement period until the medical staff reconnects the electrodes. This work would improve the clinical practicability of dynamic brain EIT and contribute to its further promotion.
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Affiliation(s)
- Ge Zhang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Meng Dai
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Lin Yang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Weichen Li
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Haoting Li
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Canhua Xu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Xuetao Shi
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Xiuzhen Dong
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
| | - Feng Fu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
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Gan R, Chen N, Huang D. Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models. PeerJ 2016; 4:e2684. [PMID: 27843718 PMCID: PMC5103820 DOI: 10.7717/peerj.2684] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 10/13/2016] [Indexed: 02/04/2023] Open
Abstract
This study compares and evaluates the prediction of hepatitis in Guangxi Province, China by using back propagation neural networks based genetic algorithm (BPNN-GA), generalized regression neural networks (GRNN), and wavelet neural networks (WNN). In order to compare the results of forecasting, the data obtained from 2004 to 2013 and 2014 were used as modeling and forecasting samples, respectively. The results show that when the small data set of hepatitis has seasonal fluctuation, the prediction result by BPNN-GA will be better than the two other methods. The WNN method is suitable for predicting the large data set of hepatitis that has seasonal fluctuation and the same for the GRNN method when the data increases steadily.
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Affiliation(s)
- Ruijing Gan
- School of Preclinical Medicine, Guangxi Medical University , Nanning, Guangxi , China
| | - Ni Chen
- School of Preclinical Medicine, Guangxi Medical University , Nanning, Guangxi , China
| | - Daizheng Huang
- School of Preclinical Medicine, Guangxi Medical University , Nanning, Guangxi , China
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