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Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network

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Abstract

The paper focuses on the development of a reliable medical expert system for diagnosis of low back pain (LBP) by proposing an efficient frame-based knowledge representation scheme and a suitable resolution logic with conflicts in outcomes being resolved using Bayesian network. Considering that LBP is classified into many diseases based on different pain generators, the proposed methodology infers non-conflicting LBP diseases sorted according to their chances of occurrence. A satisfactory clinical efficacy (average relative error − 0.09, recall 74.44%, precision 76.67%, accuracy 71.11%, and F1-score 73.88%) of the proposed methodology has been found after validating the design with empirically selected thirty LBP patient cases. Constraining that an inferred disease having chance of occurrence, prior to pathological investigations, below 0.75 (as set by four pain specialists) is not accepted clinically; the design can correctly identify, on average, 74.44% of actual diagnosis; and 76.67% of inferred diagnosis is included in actual diagnosis. With the predicted chance of occurrence being lower than 0.75 by a fraction of 0.09 on average, the proposed design performs well for 73.88% cases detecting 71.11% inferred outcomes as accurate. The design offers homogeneity to the actual outcomes, with the chi-squared static being calculated as 11.08 having 12 as degree of freedom.

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Acknowledgments

We would like to thank the Editor-in-Chief, and the Associate Editor of the journal of Medical and Biological Engineering and Computing for being supportive towards the publication of this paper. We show our sincere gratitude to the anonymous reviewers whose valuable comments helped us greatly to improve the presentation. The authors are sincerely thankful to the director and other faculty members at the ESI Institute of Pain Management, ESI Hospital Sealdah, West Bengal, India for providing exhaustive domain knowledge. Also, the authors are very grateful to the hospital authority (ESI Hospital Sealdah) and the members of the ethics committee for supporting this research by allowing to access sufficient patient records. Finally, special thanks go to Sounak Sadhukhan, PhD scholar, Department of Computer Science, Banaras Hindu University, Varanasi, 221005, Uttar Pradesh, India, for his valuable inputs during preparation of the revised manuscript.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Engineering, Technology and Management, University of Kalyani, Block C, Nadia, Kalyani, West Bengal, 741245, India

    Debarpita Santra & Jyotsna Kumar Mandal

  2. Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India

    Swapan Kumar Basu

  3. ESI Institute of Pain Management,, ESI Hospital Sealdah premises, 301/3 Acharya Prafulla Chandra Road, Kolkata, West Bengal, 700009, India

    Subrata Goswami

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  1. Debarpita Santra
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  2. Jyotsna Kumar Mandal
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  4. Subrata Goswami
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Contributions

All the authors have significantly contributed to the study conception, design, and during the revision of the manuscript. Material preparation, data collection, and analysis were performed by Debarpita Santra, and Jyotsna Kumar Mandal. Swapan Kumar Basu provided his valuable technical inputs and research idea during preparation of the manuscript. Subrata Goswami supplied relevant knowledge about the domain of low back pain and checked the correctness of the medical concepts used in the paper. All the authors have significant contributions while revising the manuscript. The final version has been read and approved by all of them.

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Correspondence to Debarpita Santra.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (ESI Institute of Pain Management Institutional Ethics Committee (IEC)/Institutional Review Board (IRB) + reference number: 011/2018–19) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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The case histories of thirty patients that were used during validation of the methodology proposed in this manuscript were taken with the prior ethical approval from the concerned hospital authority.

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Santra, D., Mandal, J.K., Basu, S.K. et al. Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network. Med Biol Eng Comput 58, 2737–2756 (2020). https://doi.org/10.1007/s11517-020-02222-9

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