| For: | Guan P, Huang DS, Zhou BS. Forecasting model for the incidence of hepatitis A based on artificial neural network. World J Gastroenterol 2004; 10(24): 3579-3582 [PMID: 15534910 DOI: 10.3748/wjg.v10.i24.3579] |
|---|---|
| URL: | https://www.wjgnet.com/1007-9327/full/v10/i24/3579.htm |
| Number | Citing Articles |
| 1 |
Roland N. Dickerson, Darius L. Mason, Martin A. Croce, Gayle Minard, Rex O. Brown. Evaluation of an Artificial Neural Network to Predict Urea Nitrogen Appearance for Critically Ill Multiple‐Trauma Patients. Journal of Parenteral and Enteral Nutrition 2005; 29(6) doi: 10.1177/0148607105029006429
|
| 2 |
Sandeep Samantaray, Potnuru Sumaan, Pravin Surin, Nihar Ranjan Mohanta, Abinash Sahoo. Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems 2022; 288 doi: 10.1007/978-981-16-5120-5_21
|
| 3 |
Jun Xu, Jia Guo, Hai-qiang Yang, Qing-lian Ji, Rui-jie Song, Feng Hou, Hao-yu Liang, Shun-li Liu, Lan-tian Tian, He-xiang Wang. Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors. European Radiology 2023; 33(10) doi: 10.1007/s00330-023-09686-x
|
| 4 |
Binish Fatimah, Priya Aggarwal, Pushpendra Singh, Anubha Gupta. A comparative study for predictive monitoring of COVID-19 pandemic. Applied Soft Computing 2022; 122 doi: 10.1016/j.asoc.2022.108806
|
| 5 |
Wei Liu, Xue Liu, Mei Peng, Gong-Quan Chen, Peng-Hua Liu, Xin-Wu Cui, Fan Jiang, Christoph F Dietrich. Artificial intelligence for hepatitis evaluation. World Journal of Gastroenterology 2021; 27(34): 5715-5726 doi: 10.3748/wjg.v27.i34.5715
|
| 6 |
L. Yang, Z.‐W. Bi, Z.‐Q. Kou, X.‐J. Li, M. Zhang, M. Wang, L.‐Y. Zhang, Z.‐T. Zhao. Time‐Series Analysis on Human Brucellosis During 2004–2013 in Shandong Province, China. Zoonoses and Public Health 2015; 62(3) doi: 10.1111/zph.12145
|
| 7 |
Derya Avci. An Automatic Diagnosis System for Hepatitis Diseases Based on Genetic Wavelet Kernel Extreme Learning Machine. Journal of Electrical Engineering and Technology 2016; 11(4) doi: 10.5370/JEET.2016.11.4.993
|
| 8 |
Pınar Cihan. Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the World. Applied Soft Computing 2021; 111 doi: 10.1016/j.asoc.2021.107708
|
| 9 |
Yuh-Feng Wang, Tsung-Ming Hu, Chia-Chao Wu, Fu-Chiu Yu, Chao-Ming Fu, Shih-Hua Lin, Wei-Hsin Huang, Jainn-Shiun Chiu. Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural network. Computer Methods and Programs in Biomedicine 2006; 83(2) doi: 10.1016/j.cmpb.2006.06.001
|
| 10 |
Wei Wu, Junqiao Guo, Shuyi An, Peng Guan, Yangwu Ren, Linzi Xia, Baosen Zhou, Hiroshi Nishiura. Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China. PLOS ONE 2015; 10(8) doi: 10.1371/journal.pone.0135492
|
| 11 |
Cihan ÇILGIN, Mehmet Ozan ÖZDEMİR. TIME SERIES FORECASTING OF COVID-19 CONFIRMED CASES IN TURKEY WITH STACKING ENSEMBLE MODELS. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 2023; (26) doi: 10.29029/busbed.1299248
|
| 12 |
Rupinder Katoch, Arpit Sidhu. An Application of ARIMA Model to Forecast the Dynamics of COVID-19 Epidemic in India. Global Business Review 2025; 26(2) doi: 10.1177/0972150920988653
|
| 13 |
Sana Ansari, Imran Shafi, Jamil Ahmad, Syed Ismail Shah. Determination of hepatotropic virus in human metabolism using artificial neural networks. 2010 6th International Conference on Emerging Technologies (ICET) 2010; doi: 10.1109/ICET.2010.5638390
|
| 14 |
Qiyong Liu, Xiaodong Liu, Baofa Jiang, Weizhong Yang. Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infectious Diseases 2011; 11(1) doi: 10.1186/1471-2334-11-218
|
| 15 |
Da Hye Lee, Youn Su Kim, Young Youp Koh, Kwang Yoon Song, In Hong Chang. Forecasting COVID-19 Confirmed Cases Using Empirical Data Analysis in Korea. Healthcare 2021; 9(3) doi: 10.3390/healthcare9030254
|
| 16 |
Lingling Zhou, Jing Xia, Lijing Yu, Ying Wang, Yun Shi, Shunxiang Cai, Shaofa Nie. Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. International Journal of Environmental Research and Public Health 2016; 13(4) doi: 10.3390/ijerph13040355
|
| 17 |
Philipp A. Dirkx, Thomas L.A. Heil. Investment factor timing: Harvesting the low-risk anomaly using artificial neural networks. Expert Systems with Applications 2022; 189 doi: 10.1016/j.eswa.2021.116093
|
| 18 |
J. Behnamian, S.M.T. Fatemi Ghomi. Development of a PSO–SA hybrid metaheuristic for a new comprehensive regression model to time-series forecasting. Expert Systems with Applications 2010; 37(2) doi: 10.1016/j.eswa.2009.05.079
|
| 19 |
Zeynep Ceylan. Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment 2020; 729 doi: 10.1016/j.scitotenv.2020.138817
|
| 20 |
Lijing Yu, Lingling Zhou, Li Tan, Hongbo Jiang, Ying Wang, Sheng Wei, Shaofa Nie, Lijun Rong. Application of a New Hybrid Model with Seasonal Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Neural Network (NARNN) in Forecasting Incidence Cases of HFMD in Shenzhen, China. PLoS ONE 2014; 9(6) doi: 10.1371/journal.pone.0098241
|
| 21 |
L. LIU, R. S. LUAN, F. YIN, X. P. ZHU, Q. LÜ. Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model. Epidemiology and Infection 2016; 144(1) doi: 10.1017/S0950268815001144
|
| 22 |
Maher Ala’raj, Munir Majdalawieh, Nishara Nizamuddin. Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections. Infectious Disease Modelling 2021; 6 doi: 10.1016/j.idm.2020.11.007
|
| 23 |
Akif DOĞANTEKİN, Cafer BAL. Diyabet Hastalıkları İçin GA-DÇF-UÖM Tabanlı Uzman Tanı Sistemi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 2022; 34(1) doi: 10.35234/fumbd.1031302
|
| 24 |
Michael L. Jackson. Confounding by Season in Ecologic Studies of Seasonal Exposures and Outcomes: Examples From Estimates of Mortality Due to Influenza. Annals of Epidemiology 2009; 19(10) doi: 10.1016/j.annepidem.2009.06.009
|
| 25 |
Amir Talaei-Khoei, James M Wilson, Seyed-Farzan Kazemi. Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment. JMIR Public Health and Surveillance 2019; 5(1) doi: 10.2196/11357
|
| 26 |
Lei Duan, Changjie Tang, Xiaosong Li, Guozhu Dong, Xianming Wang, Jie Zuo, Min Jiang, Zhongqi Li, Yongqing Zhang. Mining effective multi-segment sliding window for pathogen incidence rate prediction. Data & Knowledge Engineering 2013; 87 doi: 10.1016/j.datak.2013.05.006
|
| 27 |
Yan-Ling Zheng, Li-Ping Zhang, Xue-Liang Zhang, Kai Wang, Yu-Jian Zheng, Zhefeng Meng. Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China. PLOS ONE 2015; 10(3) doi: 10.1371/journal.pone.0116832
|
| 28 |
Ziqi Wu, Liya Gong, Jingwen Luo, Xiaobo Chen, Fan Yang, Junyan Wen, Yanyu Hao, Zhishan Wang, Ruozhen Gu, Yuqin Zhang, Hai Liao, Ge Wen. An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer. Insights into Imaging 2025; 16(1) doi: 10.1186/s13244-025-02009-2
|
| 29 |
A. SUMI, T. LUO, D. ZHOU, B. YU, D. KONG, N. KOBAYASHI. Time-series analysis of hepatitis A, B, C and E infections in a large Chinese city: application to prediction analysis. Epidemiology and Infection 2013; 141(5) doi: 10.1017/S095026881200146X
|
| 30 |
Lingling Zhou, Lijing Yu, Ying Wang, Zhouqin Lu, Lihong Tian, Li Tan, Yun Shi, Shaofa Nie, Li Liu, David Joseph Diemert. A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China. PLoS ONE 2014; 9(8) doi: 10.1371/journal.pone.0104875
|
| 31 |
Jainn-Shiun Chiu, Yuh-Feng Wang, Yu-Cheih Su, Ling-Huei Wei, Jian-Guo Liao, Yu-Chuan Li. Artificial Neural Network to Predict Skeletal Metastasis in Patients with Prostate Cancer. Journal of Medical Systems 2009; 33(2) doi: 10.1007/s10916-008-9168-2
|
| 32 |
Pratima Kumari, Durga Toshniwal. Real-time estimation of COVID-19 cases using machine learning and mathematical models - The case of India. 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) 2020; doi: 10.1109/ICIIS51140.2020.9342735
|
| 33 |
Adewale F. Lukman, Rauf I. Rauf, Oluwakemi Abiodun, Olajumoke Oludoun, Kayode Ayinde, Roseline O. Ogundokun. COVID-19 prevalence estimation: Four most affected African countries. Infectious Disease Modelling 2020; 5 doi: 10.1016/j.idm.2020.10.002
|
| 34 |
Zhaohui Xia, Lei Qin, Zhen Ning, Xingyu Zhang, Jie Zhang. Deep learning time series prediction models in surveillance data of hepatitis incidence in China. PLOS ONE 2022; 17(4) doi: 10.1371/journal.pone.0265660
|
| 35 |
Lei Duan, Changjie Tang, Chi Gou, Min Jiang, Jie Zuo. Advanced Data Mining and Applications. Lecture Notes in Computer Science 2011; 7121 doi: 10.1007/978-3-642-25856-5_12
|
| 36 |
Xingyu Zhang, Yuanyuan Liu, Min Yang, Tao Zhang, Alistair A. Young, Xiaosong Li, Alessandro Vespignani. Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China. PLoS ONE 2013; 8(5) doi: 10.1371/journal.pone.0063116
|
| 37 |
Mevlut Ture, Imran Kurt. Comparison of four different time series methods to forecast hepatitis A virus infection. Expert Systems with Applications 2006; 31(1) doi: 10.1016/j.eswa.2005.09.002
|
| 38 |
Ruijing Gan, Ni Chen, Daizheng Huang. Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models. PeerJ 2016; 4 doi: 10.7717/peerj.2684
|
| 39 |
Zeming Li, Yanning Li. A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS. BMC Medical Informatics and Decision Making 2020; 20(1) doi: 10.1186/s12911-020-01157-3
|
| 40 |
Zhong-Qi Li, Hong-Qiu Pan, Qiao Liu, Huan Song, Jian-Ming Wang. Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China. Infectious Diseases of Poverty 2020; 9(1) doi: 10.1186/s40249-020-00771-7
|
| 41 |
Eldon Y. Li, Chen-Yuan Tung, Shu-Hsun Chang. The wisdom of crowds in action: Forecasting epidemic diseases with a web-based prediction market system. International Journal of Medical Informatics 2016; 92 doi: 10.1016/j.ijmedinf.2016.04.014
|
| 42 |
Philipe Riskalla Leal, Ricardo José de Paula Souza e Guimarães, Milton Kampel. Associations Between Environmental and Sociodemographic Data and Hepatitis‐A Transmission in Pará State (Brazil). GeoHealth 2021; 5(5) doi: 10.1029/2020GH000327
|
| 43 |
P. D. Sreekanth, P. D. Sreedevi, Shakeel Ahmed, N. Geethanjali. Comparison of FFNN and ANFIS models for estimating groundwater level. Environmental Earth Sciences 2011; 62(6) doi: 10.1007/s12665-010-0617-0
|
| 44 |
Hadi Bagheri, Leili Tapak, Manoochehr Karami, Zahra Hosseinkhani, Hamidreza Najari, Safdar Karimi, Zahra Cheraghi, Esteban Tlelo-Cuautle. Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019. PLOS ONE 2020; 15(5) doi: 10.1371/journal.pone.0232910
|