These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

274 related articles for article (PubMed ID: 20193238)

  • 1. [Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever].
    Zhu Y; Xia JL; Wang J
    Zhonghua Liu Xing Bing Xue Za Zhi; 2009 Sep; 30(9):964-8. PubMed ID: 20193238
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study.
    Wang YW; Shen ZZ; Jiang Y
    BMJ Open; 2019 Jun; 9(6):e025773. PubMed ID: 31209084
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China.
    Wei W; Jiang J; Liang H; Gao L; Liang B; Huang J; Zang N; Liao Y; Yu J; Lai J; Qin F; Su J; Ye L; Chen H
    PLoS One; 2016; 11(6):e0156768. PubMed ID: 27258555
    [TBL] [Abstract][Full Text] [Related]  

  • 4. [Study on the epidemiological characteristics and incidence trend of scarlet fever in Shanghai, 2005-2012].
    Ren H; Wang Y; Chen ML; Yuan ZA; Li YT; Huang P; Hu JY
    Zhonghua Liu Xing Bing Xue Za Zhi; 2013 Jul; 34(7):706-10. PubMed ID: 24257173
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population.
    Li Z; Wang Z; Song H; Liu Q; He B; Shi P; Ji Y; Xu D; Wang J
    Infect Drug Resist; 2019; 12():1011-1020. PubMed ID: 31118707
    [No Abstract]   [Full Text] [Related]  

  • 6. Impact of meteorological changes on the incidence of scarlet fever in Hefei City, China.
    Duan Y; Huang XL; Wang YJ; Zhang JQ; Zhang Q; Dang YW; Wang J
    Int J Biometeorol; 2016 Oct; 60(10):1543-1550. PubMed ID: 26932715
    [TBL] [Abstract][Full Text] [Related]  

  • 7. [Applications of multiple seasonal autoregressive integrated moving average (ARIMA) model on predictive incidence of tuberculosis].
    Yi J; Du CT; Wang RH; Liu L
    Zhonghua Yu Fang Yi Xue Za Zhi; 2007 Mar; 41(2):118-21. PubMed ID: 17605238
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis.
    Luo T; Zhou J; Yang J; Xie Y; Wei Y; Mai H; Lu D; Yang Y; Cui P; Ye L; Liang H; Huang J
    J Med Internet Res; 2023 Oct; 25():e49400. PubMed ID: 37902815
    [TBL] [Abstract][Full Text] [Related]  

  • 9. [Establishing and applying of autoregressive integrated moving average model to predict the incidence rate of dysentery in Shanghai].
    Li J; Wu HY; Li YT; Jin HM; Gu BK; Yuan ZA
    Zhonghua Yu Fang Yi Xue Za Zhi; 2010 Jan; 44(1):48-53. PubMed ID: 20388364
    [TBL] [Abstract][Full Text] [Related]  

  • 10. [Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].
    Ke-Wei W; Yu W; Jin-Ping L; Yu-Yu J
    Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi; 2016 Jul; 28(6):630-634. PubMed ID: 29469251
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China.
    Wei W; Jiang J; Gao L; Liang B; Huang J; Zang N; Ning C; Liao Y; Lai J; Yu J; Qin F; Chen H; Su J; Ye L; Liang H
    Am J Trop Med Hyg; 2017 Sep; 97(3):799-805. PubMed ID: 28820678
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A hybrid model for short-term bacillary dysentery prediction in Yichang City, China.
    Yan W; Xu Y; Yang X; Zhou Y
    Jpn J Infect Dis; 2010 Jul; 63(4):264-70. PubMed ID: 20657066
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.
    Wu W; Guo J; An S; Guan P; Ren Y; Xia L; Zhou B
    PLoS One; 2015; 10(8):e0135492. PubMed ID: 26270814
    [TBL] [Abstract][Full Text] [Related]  

  • 14. [Study on the feasibility for ARIMA model application to predict malaria incidence in an unstable malaria area].
    Zhu JM; Tang LH; Zhou SS; Huang F
    Zhongguo Ji Sheng Chong Xue Yu Ji Sheng Chong Bing Za Zhi; 2007 Jun; 25(3):232-6. PubMed ID: 18038786
    [TBL] [Abstract][Full Text] [Related]  

  • 15. [Zeng-Ding phenomenon: further demonstration and studies on its predictive value in epidemic of measles and scarlet fever].
    Cheng Y; Zeng G
    Zhonghua Liu Xing Bing Xue Za Zhi; 1999 Aug; 20(4):200-3. PubMed ID: 10682494
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Application of seasonal auto-regressive integrated moving average model in forecasting the incidence of hand-foot-mouth disease in Wuhan, China.
    Peng Y; Yu B; Wang P; Kong DG; Chen BH; Yang XB
    J Huazhong Univ Sci Technolog Med Sci; 2017 Dec; 37(6):842-848. PubMed ID: 29270741
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China.
    Wang G; Wei W; Jiang J; Ning C; Chen H; Huang J; Liang B; Zang N; Liao Y; Chen R; Lai J; Zhou O; Han J; Liang H; Ye L
    Epidemiol Infect; 2019 Jan; 147():e194. PubMed ID: 31364559
    [TBL] [Abstract][Full Text] [Related]  

  • 18. [Application of time series analysis in the prediction of incidence trend of influenza-like illness in Shanghai].
    Li YT; Zhang HW; Ren H; Chen J; Wang Y
    Zhonghua Yu Fang Yi Xue Za Zhi; 2007 Nov; 41(6):496-8. PubMed ID: 18399133
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Application of a hybrid model for predicting the incidence of tuberculosis in Hubei, China.
    Zhang G; Huang S; Duan Q; Shu W; Hou Y; Zhu S; Miao X; Nie S; Wei S; Guo N; Shan H; Xu Y
    PLoS One; 2013; 8(11):e80969. PubMed ID: 24223232
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Forecasting model for the incidence of hepatitis A based on artificial neural network.
    Guan P; Huang DS; Zhou BS
    World J Gastroenterol; 2004 Dec; 10(24):3579-82. PubMed ID: 15534910
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.