IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v42y2021i4d10.1007_s10878-019-00478-y.html
   My bibliography  Save this article

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

Author

Listed:
  • Jing Yu

    (National University of Defense Technology)

  • Lining Xing

    (National University of Defense Technology)

  • Xu Tan

    (ShenZhen Institute of Information Technology)

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.

Suggested Citation

  • Jing Yu & Lining Xing & Xu Tan, 2021. "The new treatment mode research of hepatitis B based on ant colony algorithm," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 740-759, November.
    • Handle: RePEc:spr:jcomop:v:42:y:2021:i:4:d:10.1007_s10878-019-00478-y
    DOI: 10.1007/s10878-019-00478-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-019-00478-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-019-00478-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sven de Vries & Rakesh V. Vohra, 2003. "Combinatorial Auctions: A Survey," INFORMS Journal on Computing, INFORMS, vol. 15(3), pages 284-309, August.
    2. Robert W. Day & S. Raghavan, 2009. "Matrix Bidding in Combinatorial Auctions," Operations Research, INFORMS, vol. 57(4), pages 916-933, August.
    3. Mourad Ykhlef & Reem Alqifari, 2015. "A New Hybrid Algorithm to Solve Winner Determination Problem in Multiunit Double Internet Auction," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-10, June.
    4. Qian, Xiaohu & Fang, Shu-Cherng & Huang, Min & Wang, Xingwei, 2019. "Winner determination of loss-averse buyers with incomplete information in multiattribute reverse auctions for clean energy device procurement," Energy, Elsevier, vol. 177(C), pages 276-292.
    5. Ying Yang & Shoucheng Luo & Jing Fan & Xinye Zhou & Chunyu Fu & Guochun Tang, 2019. "Study on specialist outpatient matching appointment and the balance matching model," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 20-39, January.
    6. Stan van Hoesel & Rudolf Müller, 2001. "Optimization in electronic markets: examples in combinatorial auctions," Netnomics, Springer, vol. 3(1), pages 23-33, June.
    7. Jing Li & Ming Dong & Yijiong Ren & Kaiqi Yin, 2015. "How patient compliance impacts the recommendations for colorectal cancer screening," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 920-937, November.
    8. Luca Maria Gambardella & Marco Dorigo, 2000. "An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem," INFORMS Journal on Computing, INFORMS, vol. 12(3), pages 237-255, August.
    9. Michael H. Rothkopf & Aleksandar Pekev{c} & Ronald M. Harstad, 1998. "Computationally Manageable Combinational Auctions," Management Science, INFORMS, vol. 44(8), pages 1131-1147, August.
    10. Bowen Jiang & Jiafu Tang & Chongjun Yan, 2019. "A comparison of fixed and variable capacity-addition policies for outpatient capacity allocation," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 150-182, January.
    11. Yanqin Bai & Xiao Han & Tong Chen & Hua Yu, 2015. "Quadratic kernel-free least squares support vector machine for target diseases classification," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 850-870, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing Yu & Lining Xing & Xu Tan, 0. "The new treatment mode research of hepatitis B based on ant colony algorithm," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-20.
    2. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 0. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-22.
    3. Dries R. Goossens & Rudolf Müller & Frits C. R. Spieksma, 2010. "Algorithms for Recognizing Economic Properties in Matrix Bid Combinatorial Auctions," INFORMS Journal on Computing, INFORMS, vol. 22(3), pages 339-352, August.
    4. Drexl, Andreas & Jørnsten, Kurt & Knof, Diether, 2009. "Non-linear anonymous pricing combinatorial auctions," European Journal of Operational Research, Elsevier, vol. 199(1), pages 296-302, November.
    5. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 2021. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 966-987, November.
    6. Hammami, Farouk & Rekik, Monia & Coelho, Leandro C., 2021. "Exact and hybrid heuristic methods to solve the combinatorial bid construction problem with stochastic prices in truckload transportation services procurement auctions," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 204-229.
    7. Fernanda Nakano Kazama & Aluizio Fausto Ribeiro Araujo & Paulo Barros Correia & Elaine Guerrero-Peña, 2021. "Constraint-guided evolutionary algorithm for solving the winner determination problem," Journal of Heuristics, Springer, vol. 27(6), pages 1111-1150, December.
    8. Huang, George Q. & Xu, Su Xiu, 2013. "Truthful multi-unit transportation procurement auctions for logistics e-marketplaces," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 127-148.
    9. Mishra, Debasis & Parkes, David C., 2007. "Ascending price Vickrey auctions for general valuations," Journal of Economic Theory, Elsevier, vol. 132(1), pages 335-366, January.
    10. J. Behnamian & Z. Gharabaghli, 2023. "Multi-objective outpatient scheduling in health centers considering resource constraints and service quality: a robust optimization approach," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-35, March.
    11. Bourbeau, Benoit & Gabriel Crainic, Teodor & Gendreau, Michel & Robert, Jacques, 2005. "Design for optimized multi-lateral multi-commodity markets," European Journal of Operational Research, Elsevier, vol. 163(2), pages 503-529, June.
    12. Oktay Günlük & Lászlo Ladányi & Sven de Vries, 2005. "A Branch-and-Price Algorithm and New Test Problems for Spectrum Auctions," Management Science, INFORMS, vol. 51(3), pages 391-406, March.
    13. Ervasti, Valtteri & Leskelä, Riikka-Leena, 2010. "Allocative efficiency in simulated multiple-unit combinatorial auctions with quantity support," European Journal of Operational Research, Elsevier, vol. 203(1), pages 251-260, May.
    14. Richard Li-Yang Chen & Shervin AhmadBeygi & Amy Cohn & Damian R. Beil & Amitabh Sinha, 2009. "Solving Truckload Procurement Auctions Over an Exponential Number of Bundles," Transportation Science, INFORMS, vol. 43(4), pages 493-510, November.
    15. Song, Jiongjiong & Regan, Amelia, 2005. "Approximation algorithms for the bid construction problem in combinatorial auctions for the procurement of freight transportation contracts," Transportation Research Part B: Methodological, Elsevier, vol. 39(10), pages 914-933, December.
    16. Park, Sunju & Rothkopf, Michael H., 2005. "Auctions with bidder-determined allowable combinations," European Journal of Operational Research, Elsevier, vol. 161(2), pages 399-415, March.
    17. Nguyen, Tri-Dung, 2014. "A fast approximation algorithm for solving the complete set packing problem," European Journal of Operational Research, Elsevier, vol. 237(1), pages 62-70.
    18. Gediminas Adomavicius & Alok Gupta & Mochen Yang, 2022. "Bidder Support in Multi-item Multi-unit Continuous Combinatorial Auctions: A Unifying Theoretical Framework," Information Systems Research, INFORMS, vol. 33(4), pages 1174-1195, December.
    19. Jawad Abrache & Teodor Crainic & Michel Gendreau & Monia Rekik, 2007. "Combinatorial auctions," Annals of Operations Research, Springer, vol. 153(1), pages 131-164, September.
    20. Drexl, Andreas & Jørnsten, Kurt & Knof, Diether, 2007. "Column aggregation-based pricing combinatorial auctions," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 624, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:42:y:2021:i:4:d:10.1007_s10878-019-00478-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.