IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v42y2021i4d10.1007_s10878-019-00504-z.html
   My bibliography  Save this article

Early warning of venous thromboembolism after surgery based on self-organizing competitive network

Author

Listed:
  • Shi Yin

    (Shanghai Polytechnic University)

  • Jian Chang

    (Shanghai General Hospital)

  • Hailan Pan

    (Shanghai Polytechnic University)

  • Haizhou Mao

    (Zhejiang Industry and Trade Vocational College)

  • Mei Wang

    (Shanghai General Hospital)

Abstract

Postoperative early warning of venous thromboembolism (VTE) has long been determined by the clinical observations of the patient by the attending doctor. To further investigate preoperative and postoperative pathological VTE data, in this paper, we propose an improved self-organising competitive network (WSOM) algorithm based on the weighted selection of the initial connection. An early-warning model is established based on 14 factors before and after surgery for VTE patients. To verify its validity, the model was further tested on sample data. The results show that the prediction of VTE based on the WSOM algorithm and the 14 factors achieved high accuracy. The proposed WSOM can effectively screen explanatory variables for postoperative early warning of VTE, while also improving the accuracy of the postoperative early warning of VTE.

Suggested Citation

  • Shi Yin & Jian Chang & Hailan Pan & Haizhou Mao & Mei Wang, 2021. "Early warning of venous thromboembolism after surgery based on self-organizing competitive network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 909-927, November.
    • Handle: RePEc:spr:jcomop:v:42:y:2021:i:4:d:10.1007_s10878-019-00504-z
    DOI: 10.1007/s10878-019-00504-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-019-00504-z
    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/s10878-019-00504-z?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. Gang Du & Luyao Zheng & Xiaoling Ouyang, 2019. "Real-time scheduling optimization considering the unexpected events in home health care," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 196-220, January.
    2. 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.
    3. Wei Gao & Wuping Bao & Xin Zhou, 2019. "Analysis of cough detection index based on decision tree and support vector machine," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 375-384, January.
    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. 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.
    2. 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.
    3. Shi Yin & Jian Chang & Hailan Pan & Haizhou Mao & Mei Wang, 0. "Early warning of venous thromboembolism after surgery based on self-organizing competitive network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-19.
    4. Yan Li & Xiao Xu & Fuyu Wang, 2023. "Research on Home Health Care Scheduling Considering Synchronous Access of Caregivers and Vehicles," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    5. Xuerui Gao & Yanqin Bai & Qian Li, 2021. "A sparse optimization problem with hybrid $$L_2{\text {-}}L_p$$ L 2 - L p regularization for application of magnetic resonance brain images," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 760-784, November.
    6. Malek Ebadi & Raha Akhavan-Tabatabaei, 2021. "Personalized Cotesting Policies for Cervical Cancer Screening: A POMDP Approach," Mathematics, MDPI, vol. 9(6), pages 1-20, March.
    7. Paola Cappanera & Maria Grazia Scutellà, 2022. "Addressing consistency and demand uncertainty in the Home Care planning problem," Flexible Services and Manufacturing Journal, Springer, vol. 34(1), pages 1-39, March.
    8. Jing Fan & Hui Shi, 0. "A three-stage supply chain scheduling problem based on the nursing assistants’ daily work in a hospital," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-13.
    9. 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.
    10. Jalel Euchi & Malek Masmoudi & Patrick Siarry, 2022. "Home health care routing and scheduling problems: a literature review," 4OR, Springer, vol. 20(3), pages 351-389, September.
    11. Xin Yan & Hongmiao Zhu & Jian Luo, 0. "A novel kernel-free nonlinear SVM for semi-supervised classification in disease diagnosis," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-18.
    12. Xin Yan & Hongmiao Zhu & Jian Luo, 2021. "A novel kernel-free nonlinear SVM for semi-supervised classification in disease diagnosis," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 948-965, November.
    13. Bin Li & Qianghua Wei & Xinye Zhou, 0. "Research on model and algorithm of TCM constitution identification based on artificial intelligence," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-16.
    14. Jing Fan & Hui Shi, 2021. "A three-stage supply chain scheduling problem based on the nursing assistants’ daily work in a hospital," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 896-908, November.
    15. Xuerui Gao & Yanqin Bai & Qian Li, 0. "A sparse optimization problem with hybrid $$L_2{\text {-}}L_p$$L2-Lp regularization for application of magnetic resonance brain images," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-25.
    16. He Huang & Wei Gao & Chunming Ye, 2021. "An intelligent data-driven model for disease diagnosis based on machine learning theory," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 884-895, November.
    17. 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.
    18. Ruiping Wang & Mei Wang & Jian Chang & Zai Luo & Feng Zhang & Chen Huang, 2021. "An optimized approach of venous thrombus embolism risk assessment," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 1053-1063, November.
    19. Bin Li & Qianghua Wei & Xinye Zhou, 2021. "Research on model and algorithm of TCM constitution identification based on artificial intelligence," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 988-1003, November.
    20. Ruiping Wang & Mei Wang & Jian Chang & Zai Luo & Feng Zhang & Chen Huang, 0. "An optimized approach of venous thrombus embolism risk assessment," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-11.

    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_s10878-019-00504-z. 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.