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

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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.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant Nos. 72171066, 72101074, and 72188101) and the Fundamental Research Funds for the Central Universities (Grant No. JZ2021HGTA0139).

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Box 270, Hefei, 230009, Anhui, China

    Chao Fu, Dongyue Wang & Wenjun Chang

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, 230009, Anhui, China

    Chao Fu, Dongyue Wang & Wenjun Chang

  3. Ministry of Education Engineerfing Research Center for Intelligent Decision-Making & Information System Technologies, Hefei, 230009, China

    Chao Fu, Dongyue Wang & Wenjun Chang

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  1. Chao Fu
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Fu, C., Wang, D. & Chang, W. Data-driven analysis of influence between radiologists for diagnosis of breast lesions. Ann Oper Res 328, 419–449 (2023). https://doi.org/10.1007/s10479-022-05086-4

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