IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v328y2023i1d10.1007_s10479-022-05086-4.html
   My bibliography  Save this article

Data-driven analysis of influence between radiologists for diagnosis of breast lesions

Author

Listed:
  • Chao Fu

    (Hefei University of Technology
    Ministry of Education
    Ministry of Education Engineerfing Research Center for Intelligent Decision-Making & Information System Technologies)

  • Dongyue Wang

    (Hefei University of Technology
    Ministry of Education
    Ministry of Education Engineerfing Research Center for Intelligent Decision-Making & Information System Technologies)

  • Wenjun Chang

    (Hefei University of Technology
    Ministry of Education
    Ministry of Education Engineerfing Research Center for Intelligent Decision-Making & Information System Technologies)

Abstract

Breast lesions are the most common threat to the health of women. The accumulation of historical examination reports for diagnosing breast lesions in clinical practice provides the necessary foundations for analyzing the diagnostic preferences of radiologists and the mutual influence between radiologists in a hospital. This mutual influence is important for indicating the development of an ultrasonic department in which radiologists work. To conduct a data-driven analysis of the influence between the two radiologists, the influence of the diagnostic preferences of one radiologist on the other was qualitatively defined using regression models. Following the qualitative definition, the process of analyzing the influence between two radiologists was designed, in which ten machine learning regression algorithms were included to make a reliable analysis. A statistical comparison method was developed using each machine learning regression algorithm to generate the indicator pair. The indicator pairs generated by ten machine learning regression algorithms were integrated using absolute majority voting to derive the overall indicator pair, from which the influence between two radiologists was determined, namely the unclear influence, clear influence, or significant influence. Experiments were conducted based on historical examination reports collected from two hospitals in Hefei, Anhui, China. The experimental results indicate that the trend in the influence between two radiologists in one hospital is different from that in the other hospital, which is associated with the management pattern, innovation incentive, and reward pattern of the two hospitals. A general conclusion on managerial insights was drawn to generalize the findings of this study.

Suggested Citation

  • Chao Fu & Dongyue Wang & Wenjun Chang, 2023. "Data-driven analysis of influence between radiologists for diagnosis of breast lesions," Annals of Operations Research, Springer, vol. 328(1), pages 419-449, September.
  • Handle: RePEc:spr:annopr:v:328:y:2023:i:1:d:10.1007_s10479-022-05086-4
    DOI: 10.1007/s10479-022-05086-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-05086-4
    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/s10479-022-05086-4?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. Llamazares, Bonifacio, 2004. "Simple and absolute special majorities generated by OWA operators," European Journal of Operational Research, Elsevier, vol. 158(3), pages 707-720, November.
    2. Guo, Mengzhuo & Liao, Xiuwu & Liu, Jiapeng & Zhang, Qingpeng, 2020. "Consumer preference analysis: A data-driven multiple criteria approach integrating online information," Omega, Elsevier, vol. 96(C).
    3. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    4. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    5. Chao Fu & Weiyong Liu & Wenjun Chang, 2020. "Data-driven multiple criteria decision making for diagnosis of thyroid cancer," Annals of Operations Research, Springer, vol. 293(2), pages 833-862, October.
    6. Mehmet Eren Ahsen & Mehmet Ulvi Saygi Ayvaci & Srinivasan Raghunathan, 2019. "When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis," Service Science, INFORMS, vol. 30(1), pages 97-116, March.
    7. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
    8. Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.
    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. Benítez-Peña, Sandra & Blanquero, Rafael & Carrizosa, Emilio & Ramírez-Cobo, Pepa, 2024. "Cost-sensitive probabilistic predictions for support vector machines," European Journal of Operational Research, Elsevier, vol. 314(1), pages 268-279.
    2. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    3. Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
    4. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    5. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    6. Che Xu & Wenjun Chang & Weiyong Liu, 2023. "Data-driven decision model based on local two-stage weighted ensemble learning," Annals of Operations Research, Springer, vol. 325(2), pages 995-1028, June.
    7. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    8. Ahmed, Abdulaziz & Topuz, Kazim & Moqbel, Murad & Abdulrashid, Ismail, 2024. "What makes accidents severe! explainable analytics framework with parameter optimization," European Journal of Operational Research, Elsevier, vol. 317(2), pages 425-436.
    9. Liu, Wanan & Fan, Hong & Xia, Meng, 2023. "Tree-based heterogeneous cascade ensemble model for credit scoring," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1593-1614.
    10. Deng, Shangkun & Luo, Qunfang & Zhu, Yingke & Ning, Hong & Shimada, Tatsuro, 2024. "Financial risk forewarning with an interpretable ensemble learning approach: An empirical analysis based on Chinese listed companies," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    11. Xing, Jin & Chi, Guotai & Pan, Ancheng, 2024. "Instance-dependent misclassification cost-sensitive learning for default prediction," Research in International Business and Finance, Elsevier, vol. 69(C).
    12. Vairetti, Carla & Gennaro, Franco & Maldonado, Sebastián, 2024. "Propensity score oversampling and matching for uplift modeling," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1058-1069.
    13. Stefan Feuerriegel & Mateusz Dolata & Gerhard Schwabe, 2020. "Fair AI," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(4), pages 379-384, August.
    14. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    15. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    16. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    17. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    18. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    19. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    20. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.

    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:annopr:v:328:y:2023:i:1:d:10.1007_s10479-022-05086-4. 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.