1,175
Views
0
CrossRef citations to date
0
Altmetric
Infectious Diseases

Development and validation of HBV surveillance models using big data and machine learning

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon show all
Article: 2314237 | Received 25 Sep 2023, Accepted 30 Jan 2024, Published online: 10 Feb 2024

References

  • World Health Organization. Global progress report on HIV, viral hepatitis and sexually transmitted infections., 2021. Geneva: WHO; 2021.
  • World Health Organization. Up to 10 million people in China could die from chronic hepatitis by 2030 – urgent action needed to bring an end to the ‘silent. epidemic’. Beijing: WHO; 2016.
  • Feng J, Gong Y, Li H, et al. Development trend of primary healthcare after health reform in China: a longitudinal observational study. BMJ Open. 2022;12(6):1. doi: 10.1136/bmjopen-2021-052239.
  • National Health Commission of the People’s Republic of China. 2020. Statistical bulletin of China’s health development. National Health Commission of the People’s Republic of China; 2021.
  • Xinhua News Agency. “Healthy China 2030” plan outline. Beijing (China): Xinhua News Agency; 2016.
  • World Health Organization. Combating hepatitis B and C to reach elimination by 2030. Geneva: WHO; 2016.
  • Wong WCW, Lo YR, Jiang S, et al. Improving the hepatitis Cascade: assessing hepatitis testing and its management in primary health care in China. Fam Pract. 2018;35(6):731–15. doi: 10.1093/fampra/cmy032.
  • Liu Z, Lin C, Mao X, et al. Changing prevalence of chronic hepatitis B virus infection in China between 1973 and 2021: a systematic literature review and meta-analysis of 3740 studies and 231 million people. Gut. 2023;72(12):2354–2363. doi: 10.1136/gutjnl-2023-330691.
  • Su S, Wong WC, Zou Z, et al. Cost-effectiveness of universal screening for chronic hepatitis B virus infection in China: an economic evaluation. Lancet Glob Health. 2022;10(2):e278–e287. doi: 10.1016/S2214-109X(21)00517-9.
  • Jin D, Treloar C, Brener L. Hepatitis B virus related stigma among Chinese living in mainland China: a scoping review. Psychol Health Med. 2022;27(8):1760–1773. doi: 10.1080/13548506.2021.1944651.
  • Liang J, Li Y, Zhang Z, et al. Evaluating the applications of health information technologies in China during the past 11 years: consecutive survey data analysis. JMIR Med Inform. 2020;8(2):e17006. doi: 10.2196/17006.
  • Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):160. doi: 10.1007/s42979-021-00592-x.
  • Leng A, Li Y, Wangen KR, et al. Hepatitis B discrimination in everyday life by rural migrant workers in hongqi. Hum Vaccin Immunother. 2016;12(5):1164–1171. doi: 10.1080/21645515.2015.1131883.
  • Zou X, Chow EPF, Zhao P, et al. Rural-to-urban migrants are at high risk of sexually transmitted and viral hepatitis infections in China: a systematic review and meta-analysis. BMC Infect Dis. 2014;14(1):490. doi: 10.1186/1471-2334-14-490.
  • Tao J, Zhang W, Yue H, et al. Prevalence of hepatitis B virus infection in Shenzhen, China, 2015-2018. Sci Rep. 2019;9(1):13948–13948. doi: 10.1038/s41598-019-50173-5.
  • Wong WCW, Yang NS, Li J, et al. Crowdsourcing to promote hepatitis C testing and linkage-to-care in China: a randomized controlled trial protocol. BMC Public Health. 2020;20(1):1048. doi: 10.1186/s12889-020-09152-z.
  • Wang Y, Du Z, Lawrence WR, et al. Predicting hepatitis B virus infection based on health examination data of community population. Int J Environ Res Public Health. 2019;16(23):4842. doi: 10.3390/ijerph16234842.
  • Ajuwon BI, Richardson A, Roper K, et al. The development of a machine learning algorithm for early detection of viral hepatitis B infection in Nigerian patients. Sci Rep. 2023;13(1):3244. doi: 10.1038/s41598-023-30440-2.
  • Chakraborty C, Gupta B, Ghosh SK. Mobile metadata assisted community database of chronic wound images. Wound Medicine. 2014;6:34–42. doi: 10.1016/j.wndm.2014.09.002.
  • Othman SB, Almalki FA, Chakraborty C, et al. Privacy-preserving aware data aggregation for IoT-based healthcare with green computing technologies. Comput Electr Eng. 2022;101:108025. doi: 10.1016/j.compeleceng.2022.108025.
  • Kishor A, Chakraborty C. Early and accurate prediction of diabetics based on FCBF feature selection and SMOTE. Int J Syst Assur Eng Manag. 2021. doi: 10.1007/s13198-021-01174-z.
  • Wong A, Plasek JM, Montecalvo SP, et al. Natural language processing and its implications for the future of medication safety: a narrative review of recent advances and challenges. Pharmacotherapy. 2018;38(8):822–841. doi: 10.1002/phar.2151.
  • Kades K, Sellner J, Koehler G, et al. Adapting bidirectional encoder representations from transformers (BERT) to assess clinical semantic textual similarity: algorithm development and validation study. JMIR Med Inform. 2021;9(2):e22795-e22795. doi: 10.2196/22795.
  • Barber D. Bayesian reasoning and machine learning. Cambridge: Cambridge University Press; 2012.
  • Zhong B, Xing X, Luo H, et al. Deep learning-based extraction of construction procedural constraints from construction regulations. Adv Eng Inf. 2020;43:101003. doi: 10.1016/j.aei.2019.101003.
  • Dong W, Fong DYT, Yoon J-S, et al. Generative adversarial networks for imputing missing data for big data clinical research. BMC Med Res Methodol. 2021;21(1):78. doi: 10.1186/s12874-021-01272-3.
  • Yoon J, Jordon J, van der Schaar M. GAIN: missing data imputation using generative adversarial nets. 2018.
  • OECD [Internet]. Medical Classifications. [cited 15 May 2023]. Available from: https://www.oecd-ilibrary.org/content/component/9789264270985-24-en.
  • Terrault NA, Lok ASF, McMahon BJ, et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Hepatology. 2018;67(4):1560–1599. doi: 10.1002/hep.29800.
  • Mayo M, Frank E. Improving naive bayes for regression with optimized artificial surrogate data. Appl Artif Intel. 2020;34(6):484–514. doi: 10.1080/08839514.2020.1726615.
  • Ciaburro G, Venkateswaran B. Neural networks with R: smart models using CNN, RNN, deep learning, and artificial intelligence principles. 1st ed. Birmingham (UK): PACKT Publishing; 2017.
  • Maity S, Rastogi A, Djeddi C, et al. A novel optimized method for feature selection using non-linear Kernel-Free twin quadratic surface support vector machine. In: Cham: springer International Publishing; 2021:339–353.
  • Cichosz P. Data mining algorithms: explained using R. Chichester, West Sussex: John Wiley & Sons Inc.; 2015.
  • Kavzoglu T, Teke A. Predictive performances of ensemble machine learning algorithms in landslide susceptibility mapping using random Forest, extreme gradient boosting (XGBoost) and natural gradient boosting (NGBoost). Arab J Sci Eng. 2022;47(6):7367–7385. doi: 10.1007/s13369-022-06560-8.
  • Singh M, Tyagi V, Gupta PK, et al. Accelerating the performance of sequence classification using GPU based ensemble learning with extreme gradient boosting. In: vol 1613. Switzerland: Springer International Publishing AG; 2022:257–268.
  • Wang B, Li C, Pavlu V, et al. Regularizing model complexity and label structure for Multi-Label text classification. 2017.
  • Cameron AR, Meyer A, Faverjon C, et al. Quantification of the sensitivity of early detection surveillance. Transbound Emerg Dis. 2020;67(6):2532–2543. doi: 10.1111/tbed.13598.
  • Habibzadeh F, Habibzadeh P, Yadollahie M. On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochem Med (Zagreb). 2016;26(3):297–307. doi: 10.11613/BM.2016.034.
  • Merrick L, Taly A. The explanation game: explaining machine learning models using Shapley values. In: Holzinger A, Kieseberg P, Tjoa AM, Weippl E, editors. International Cross-Domain Conference for Machine Learning and Knowledge Extraction. Cham: Springer International Publishing; 2020. pp. 17-–38. doi: 10.1007/978-3-030-57321-8_2
  • Lee N, Kim J-M. Conversion of categorical variables into numerical variables via Bayesian network classifiers for binary classifications. Computational Statistics & Data Analysis. 2010;54(5):1247–1265. doi: 10.1016/j.csda.2009.11.003.
  • Eiseman NA, Bianchi MT, Westover MB. The information theoretic perspective on medical diagnostic inference. Hosp Pract (1995). 2014;42(2):125–138. doi: 10.3810/hp.2014.04.1110.
  • NIH. [Internet]. Platelet disorders. Bethesda: National Heart, Lung, and Blood Institute; [cited 15 May 2023]. Available from: //www.nhlbi.nih.gov/health/thrombocytopenia
  • Yin J, Tian L. Joint inference about sensitivity and specificity at the optimal cut-off point associated with youden index. Computational Statistics & Data Analysis. 2014;77:1–13. doi: 10.1016/j.csda.2014.01.021.
  • Terrault NA, Bzowej NH, Chang KM, et al. AASLD guidelines for treatment of chronic hepatitis B. Hepatology. 2016;63(1):261–283. doi: 10.1002/hep.28156.
  • Marley G, Seto WK, Yan W, et al. What facilitates hepatitis B and hepatitis C testing and the role of stigma among primary care patients in China? J Viral Hepat. 2022;29(8):637–645. doi: 10.1111/jvh.13711.
  • Zhou X, Zhang F, Ao Y, et al. Diagnosis experiences from 50 hepatitis B patients in Chongqing, China: a qualitative study. BMC Public Health. 2021;21(1):2195. doi: 10.1186/s12889-021-11929-9.
  • Devlin J, Chang M-W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. 2019. arXiv preprint arXiv:181004805.
  • Ouyang L, Wu J, Xu J, et al. Training language models to follow instructions with human feedback. In. Ithaca: Cornell University Library, arXiv.org; 2022.
  • Lee MH, Yang HI, Liu J, et al. Prediction models of long-term cirrhosis and hepatocellular carcinoma risk in chronic hepatitis B patients: risk scores integrating host and virus profiles. Hepatology. 2013;58(2):546–554. doi: 10.1002/hep.26385.
  • Kao JH. Risk stratification of HBV infection in Asia-Pacific region. Clin Mol Hepatol. 2014;20(3):223–227. doi: 10.3350/cmh.2014.20.3.223.