Skip to main content
Springer Nature Link
Log in

The new treatment mode research of hepatitis B based on ant colony algorithm

  • Published:
Journal of Combinatorial Optimization Aims and scope Submit manuscript

Abstract

Hepatitis B (HB) is a deadly disease that has a severe impact on infected individuals. In China, not only are the incidence and infection rates of HB very high, but also many HB patients suffer from mental illness associated with anxiety and fear because of HB-associated symptoms. This exacerbates the patients’ condition, potentially increasing the risk of mortality. In this paper, we propose a new treatment mode to improve the therapeutic efficiency and patients’ satisfaction with their healthcare. In a single process of this new treatment, several patients with similar disease symptoms are treated by one doctor at the same time. This new treatment mode can not only relieve the anxiety and fear of HB patients, and improve patients’ cognition rate of HB, but also reduce the HB infection rate, slow down the progression of disease symptoms, and shorten the course. If patients with similar disease symptoms are to be grouped together, there is a need to determine the optimal patient batch combination, which can be solved in the new mode, called patient combined problem (PCP). We also constructed a mathematical model of PCP, and present the ant colony (AC) algorithm and Enhanced AC with a P-3-exchange operator for PCP in the new treatment mode in this paper. We also performed an experiment that showed that our proposed algorithms are very fast and effective for solving this problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Adeyemi AB, Enabor OO, Ugwu IA et al (2013) Knowledge of hepatitis B virus infection, access to screening and vaccination among pregnant women in Ibadan, Nigeria. J Obstet Gynaecol 33(2):155–159

    Google Scholar 

  • Andersson A , Tenhunen M , Ygge F (2000) Integer programming for combinatorial auction winner determination. In: International conference on multiagent systems. IEEE

  • Avenali A, Bassanini A (2007) Simulating combinatorial auctions with dominance requirement and loll bids through automated agents. Decis Support Syst 43(1):211–228

    Google Scholar 

  • Bai Y, Han X, Chen T, Hua Y (2015) Quadratic kernel-free least squares support vector machine for target diseases classification, special issue on combinatorial optimization in health care. J Combin Optim 30(4):850–870

    MATH  Google Scholar 

  • Bertuccio P, Chatenoud L, Levi F et al (2010) Recent patterns in gastric cancer: a global overview. Int J Cancer 125(3):666–673

    Google Scholar 

  • Cai S (2015) Balance between complexity and quality: local search for minimum vertex cover in massive graphs. In: International conference on artificial intelligence

  • Chao SD, Chang et al (2009) The Jade Ribbon Campaign: a model program for community outreach and education to prevent liver cancer in Asian Americans. J Immigr Minor Health 11(4):281–290

    MathSciNet  Google Scholar 

  • Chen J, Li K, Tang Z et al (2016) A parallel patient treatment time prediction algorithm and its applications in hospital queuing-recommendation in a big data environment. IEEE Access 4:1767–1783

    Google Scholar 

  • Chen Z, Zeng Y, Wang L et al (2017) Study on the relationship between immune state and disease progression or diseaseseverity of patients infected with hepatitis B virus. West China Med J 01:39–44

    Google Scholar 

  • Chen X, Zhao L, Liang H et al (2017) Matching patients and healthcare service providers: a novel two-stage method based on knowledge rules and OWA-NSGA-II algorithm. J Combin Optim 37(1):221–247

    MathSciNet  MATH  Google Scholar 

  • Chen J, Li K, Rong H et al (2018) A disease diagnosis and treatment recommendation system based on big data mining and cloud computing. Informationences 435:S0020025518300033

    Google Scholar 

  • Day R, Raghavan S (2009) Matrix bidding in combinatorial auctions. Smith School of Business, University of Maryland, pp 916–933

  • De Andrade CE, Toso RF, Resende MG, Miyazawa FK (2015) Biased random-key genetic algorithms for the winner determination problem in combinatorial auctions. Evol Comput 23(2):279–307

    Google Scholar 

  • De Vries S, Vohra RV (2003) Combinatorial auctions: a survey. Informs J Comput 15(3):284–309

    MathSciNet  MATH  Google Scholar 

  • Dong G (2018) Investigation on knowledge cognition of hepatitis B and compliance with antiviral treatment in patients with chronic hepatitis B. Chin J Public Health Manag 34:188(02):78–80+84

  • Dorigo M, Gambardella LM (2000) An ant colony system hybridized with a new local search for the sequential ordering problem. ORSA J Comput 12(3):237–255

    MathSciNet  MATH  Google Scholar 

  • Dorigo M, ManiezzoV, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern A Publ IEEE Syst Man Cybern Soc 26(1):29

    Google Scholar 

  • Gan R-w, Uo Q-s, Hang H-y et al (2009) Heuristic rules and improved ant colony optimization algorithm for winner determination. J Chin Comput Syst 30(8):1000–1220

    Google Scholar 

  • Guan R, Lui HF (2011) Treatment of hepatitis B in decompensated liver cirrhosis. Int J Hepatol 2011:1–11

    Google Scholar 

  • Hajarizadeh B, Wallace J, Richmond J et al (2015) Hepatitis B knowledge and associated factors among people with chronic hepatitis B. Aust N Z J Public Health 39(6):563–568

    Google Scholar 

  • He H-y, Wu W-s, Zhao Y et al (2019) Current situation of the hepatitis B-related knowledge of the parents of HBV carriers and its effect on the mental state. Mod Prevent Med 46(01):149–152

    Google Scholar 

  • Hoe AKC, Fong LY (2017) Bone scintigraphy and tenofovir-induced osteomalacia in chronic hepatitis B. Nucl Med Mol Imaging 51(2):1–2

    Google Scholar 

  • Hong L-y, Ni Z-x, Hu Z-f et al (2018) Investigation and analysis on the knowledge–attitude–practice (KAP) and influencing factors of hepatitis B prevention among the hepatitis B outpatients. Mod Prevent Med 8:1445–1448

    Google Scholar 

  • Jha S, Devaliya D, Bergson S et al (2016) Hepatitis B knowledge among women of childbearing age in three slums in Mumbai: a cross-sectional survey. Hepatol Med Policy 1(1):1–8

    Google Scholar 

  • Jiang B, Tang J, Yan C (2019) A comparison of fixed and variable capacity-addition policies for outpatient capacity allocation. J Combin Optim 37(1):150–182

    MathSciNet  MATH  Google Scholar 

  • Li Y-z (2009) Improved monkey-king genetic algorithm for solving winner determination in combinatiorial aructions. Comput Knowl Technol 5(23):6459–6461

    Google Scholar 

  • Li J, Dong M, Ren Y, Yin K (2015) How patient compliance impacts the recommendations for colorectal cancer screening, special issue on combinatorial optimization in health care. J Combin Optim 30(4):920–937

    MATH  Google Scholar 

  • Liaw YF, Kao JH, Piratvisuth T et al (2012) Asian-Pacific consensus statement on the management of chronic hepatitis B: a 2012 update. Hepatol Int 6(3):531–561

    Google Scholar 

  • Li R, Chen Q, Chen F (2017) Differential evolution algorithm with double mutation strategies for improving population. Oper Res Trans Divers 21(1):252–263

    MATH  Google Scholar 

  • Lin Y, Huang Y, Lin L (2018) Correlation analysis of improving outpatient appointment system and improving patients’ satisfaction. China Health Standard Manag 9(23):137–140

    Google Scholar 

  • Listed NA (2003) Proceedings of the European association for the study of the liver (EASL) international consensus conference on hepatitis B September 14–16, 2002. J Hepatol 39(Suppl 1):S1

    Google Scholar 

  • Pham TTH, Le TX, Nguyen DT, Luu CM, Truong BD, Tran PD, Toy M, So S (2019) Knowledge, attitudes and practices of hepatitis B prevention and immunization of pregnant women and mothers in northern Vietnam. PloS One 14(4):1

    Google Scholar 

  • Poynard T, Yuen M, Ratziu V et al (2003) Viral hepatitis C. Lancet 362(9401):2095–2100

    Google Scholar 

  • Proctor S, Khakoo MA (2005) Ant colony optimization by Marco Dorigo and Thomas st\(\ddot{u}\)tzle, MIT Press, 305 pp. 92-93, ISBN 0-262-04219-3. Knowl Eng Rev 20(1):92–93

    Google Scholar 

  • Qian X, Fang S-C, Huang M et al (2019) Winner determination of loss-averse buyers with incomplete information in multiattribute reverse auctions for clean energy device procurement. Energy 177:276–292

    Google Scholar 

  • Qian X, Huang M, Gao T, Wang X (2014) An improved ant colony algorithm for winner determination in multi-attribute combinatorial reverse auction. In: Evolutionary computation

  • Ren L (2015) Research progress on clinical application of group psychological therapy. China J Health Psychol 23(8):1005–1252

    Google Scholar 

  • Rothkopf MH, Pekec A, Harstad RM (1998) Computationally manageable combinatorial auctions, pp 45–80

  • Sandholm T (1999) An algorithm for optimal winner determination in combinatorial auction. In: Proceedings of the sixteenth international joint conference on artificial intelligence (IJCAI-99). Morgan Kaufmann Publishers Inc

  • Sandholm T (2000) Approaches to winner determination in combinatorial auctions. Elsevier Science Publishers B. V, Amsterdam

    Google Scholar 

  • Shil SK, Sadaoui S (2018) Meeting peak electricity demand through combinatorial reverse auctioning. J Mod Power Syst Clean Energy 6(10):1–12

    Google Scholar 

  • Skinderowicz R (2017) An improved ant colony system for the sequential ordering problem. Comput Oper Res https://doi.org/10.1016/j.cor.2017.04.012

  • Van HS, Muller R (2001) Optimization in electronic markets: examples in combinatorial auctions. Netnomics 3(1):23–33

    Google Scholar 

  • Veldhuijzen IK, Wolter R, Rijckborst V et al (2012) Identification and treatment of chronic hepatitis B in Chinese migrants: results of a project offering on-site testing in Rotterdam, The Netherlands. J Hepatol 57(6):1171–1176

    Google Scholar 

  • Wang Z, Ma F (2008) Improved discrete PSO algorithm and its application in winner determination problem. Comput Appl 28(10):2521–2524

    MATH  Google Scholar 

  • Wang D, Na W (2015) Quantum computation based bundling optimization for combinatorial auction in freight service procurements. Comput Ind Eng 89(C):186–193

    Google Scholar 

  • Wang FS, Fan JG, Zhang Z et al (2014) The global burden of liver disease: the major impact of China. Hepatology 60(6):2099–2108

    Google Scholar 

  • Xu Y (2012) Research on winner determination problem with an ant colony optimization algorithm. Tsinghua University Mathematics Science, PhD thesis, Beijing

  • Yang Y, Luo S, Fan J, Zhou X, Chunyu F, Tang G (2019) Study on specialist outpatient matching appointment and the balance matching model. J Combin Optim 37(1):20–39

    MathSciNet  MATH  Google Scholar 

  • Ykhlef M, Alqifari R (2015) A new hybrid algorithm to solve winner determination problem in multiunit double internet auction. Math Probl Eng 4:1–10

    MathSciNet  MATH  Google Scholar 

  • Zhang W, Management S O, University H M (2014) Reasons and countermeasures of the nervous doctor–patient relationship in China. Med Soc 27:44–46

    Google Scholar 

  • Zhang J, Xie N, Zhang X, Li W (2018) An online auction mechanism for cloud computing resource allocation and pricing based on user evaluation and cost. Future Gener Comput Syst 89:286–299

    Google Scholar 

  • Zhou X, Wang K, Wu D et al (2019) Cross entropy algorithm with multiple important sample level estimation for global optimization problems. Oper Res Trans Divers 23(01):19–31

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Systems Engineering, National University of Defense Technology, ChangSha, 400073, People’s Republic of China

    Jing Yu & Lining Xing

  2. School of Software Engineering, ShenZhen Institute of Information Technology, ShenZhen, 518172, People’s Republic of China

    Xu Tan

Authors
  1. Jing Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lining Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xu Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lining Xing.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This study is supported by the National Natural Science Foundation of China (61773120, 61873328), and the National Natural Science Fund for Distinguished Young Scholars of China (61525304). It is also supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China (2014-92) and the Pengcheng Scholar Funded Scheme.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, J., Xing, L. & Tan, X. The new treatment mode research of hepatitis B based on ant colony algorithm. J Comb Optim 42, 740–759 (2021). https://doi.org/10.1007/s10878-019-00478-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10878-019-00478-y

Keywords