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 *

127 related articles for article (PubMed ID: 24641853)

  • 1. [ARIMA models to predict new-diagnosing cases of pneumoconiosis in Nanjing].
    Zhong Q; Sui Y; Pang Y; Song H
    Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi; 2014 Mar; 32(3):211-3. PubMed ID: 24641853
    [TBL] [Abstract][Full Text] [Related]  

  • 2. [Autoregressive integrated moving average model in predicting road traffic injury in China].
    Pang YY; Zhang XJ; Tu ZB; Cui MJ; Gu Y
    Zhonghua Liu Xing Bing Xue Za Zhi; 2013 Jul; 34(7):736-9. PubMed ID: 24257181
    [TBL] [Abstract][Full Text] [Related]  

  • 3. [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]  

  • 4. [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]  

  • 5. [Application of multiple seasonal autoregressive integrated moving average model in predicting the mumps incidence].
    Hui S; Chen L; Liu F; Ouyang Y
    Zhonghua Yu Fang Yi Xue Za Zhi; 2015 Dec; 49(12):1042-6. PubMed ID: 26887296
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China.
    Lou HR; Wang X; Gao Y; Zeng Q
    BMC Public Health; 2022 Nov; 22(1):2167. PubMed ID: 36434563
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Exploration of Three Incidence Trend Prediction Models Based on the Number of Diagnosed Pneumoconiosis Cases in China From 2000 to 2019.
    Zhou D; Zhu D; Li N; Han B
    J Occup Environ Med; 2021 Jul; 63(7):e440-e444. PubMed ID: 34184661
    [TBL] [Abstract][Full Text] [Related]  

  • 8. [Application of ARIMA model in predicting the incidence of tuberculosis in China from 2018 to 2019].
    Yan CQ; Wang RB; Liu HC; Jiang Y; Li MC; Yin SP; Xiao TY; Wan KL; Rang WQ
    Zhonghua Liu Xing Bing Xue Za Zhi; 2019 Jun; 40(6):633-637. PubMed ID: 31238610
    [No Abstract]   [Full Text] [Related]  

  • 9. Application of an autoregressive integrated moving average model for predicting injury mortality in Xiamen, China.
    Lin Y; Chen M; Chen G; Wu X; Lin T
    BMJ Open; 2015 Dec; 5(12):e008491. PubMed ID: 26656013
    [TBL] [Abstract][Full Text] [Related]  

  • 10. [Analyses on the characteristics and the trends of pneumoconiosis notified between 1997 and 2009, in China].
    Zhang M; Wang D; Zheng YD; DU XY; Chen SY
    Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi; 2013 May; 31(5):321-34. PubMed ID: 23803520
    [TBL] [Abstract][Full Text] [Related]  

  • 11. [Study on the characteristics of temporal distribution and the epidemic trend of autumn-winter type scrub typhus under time series analysis].
    Ding L; Ding SJ; Zhang M; Wang XJ; Li Z; Zhao ZT
    Zhonghua Liu Xing Bing Xue Za Zhi; 2012 Jul; 33(7):698-701. PubMed ID: 22968019
    [TBL] [Abstract][Full Text] [Related]  

  • 12. [A study of GM (1, 1) model for predicting the incidence trends of pneumoconiosis cases of an area].
    Tan Q; Gu C; Guo Y; Wu J; Chen S; Liu Y
    Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi; 2014 Nov; 32(11):834-6. PubMed ID: 25579029
    [TBL] [Abstract][Full Text] [Related]  

  • 13. [The analyze the epidemic trend and predict the incidence trend of occupational diseases in Guangdong province].
    Li XD; Qu HY; Wen XZ; Wen CJ; Zhou SY; Yu HW
    Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi; 2018 Jul; 36(7):508-511. PubMed ID: 30248764
    [No Abstract]   [Full Text] [Related]  

  • 14. 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]  

  • 15. [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]  

  • 16. [Autoregressive integrated moving average model in food poisoning prediction in Hunan Province].
    Chen L; Xu H
    Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2012 Feb; 37(2):142-6. PubMed ID: 22561430
    [TBL] [Abstract][Full Text] [Related]  

  • 17. [Application of ARIMA model on prediction of malaria incidence].
    Jing X; Hua-Xun Z; Wen L; Su-Jian P; Ling-Cong S; Xiao-Rong D; Mu-Min C; Dong-Ni W; Shunxiang C
    Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi; 2016 Jan; 28(2):135-140. PubMed ID: 29469288
    [TBL] [Abstract][Full Text] [Related]  

  • 18. [Application of ARIMA model to predict number of malaria cases in China].
    Hui-Yu H; Hua-Qin S; Shun-Xian Z; Lin AI; Yan LU; Yu-Chun C; Shi-Zhu LI; Xue-Jiao T; Chun-Li Y; Wei HU; Jia-Xu C
    Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi; 2017 Aug; 29(4):436-440. PubMed ID: 29508575
    [TBL] [Abstract][Full Text] [Related]  

  • 19. The development of a combined mathematical model to forecast the incidence of hepatitis E in Shanghai, China.
    Ren H; Li J; Yuan ZA; Hu JY; Yu Y; Lu YH
    BMC Infect Dis; 2013 Sep; 13():421. PubMed ID: 24010871
    [TBL] [Abstract][Full Text] [Related]  

  • 20. [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]  

    [Next]    [New Search]
    of 7.