Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network | Medical & Biological Engineering & Computing Skip to main content
Springer Nature Link
Log in

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

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

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.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Kang H, Jung J, Yu J (2012) Comparison of trunk muscle activity during bridging exercises using a sling in patients with low back pain. J Sports Sci Med 11(3):510–515

    PubMed  PubMed Central  Google Scholar 

  2. Duthey B (2013) Background paper 6.24 low back pain. Priority medicines for Europe and the world. Global Burden of Disease (2010),(March), 1-29

  3. Andersson GB (1999) Epidemiological features of chronic low-back pain. Lancet 354(9178):581–585

    CAS  PubMed  Google Scholar 

  4. Hollingworth W, Todd CJ, King H, Males T, Dixon AK, Karia KR, Kinmonth AL (2002) Primary care referrals for lumbar spine radiography: diagnostic yield and clinical guidelines. Br J Gen Pract 52(479):475–480

    PubMed  PubMed Central  Google Scholar 

  5. Allegri M, Montella S, Salici F, Valente A, Marchesini M, Compagnone C et al (2016) Mechanisms of low back pain: a guide for diagnosis and therapy. F1000Research 5

  6. Werner CM, Hoch A, Gautier L, König MA, Simmen HP, Osterhoff G (2013) Distraction test of the posterior superior iliac spine (PSIS) in the diagnosis of sacroiliac joint arthropathy. BMC Surg 13(1):52

    PubMed  PubMed Central  Google Scholar 

  7. Stone JA, Bartynski WS (2009) Treatment of facet and sacroiliac joint arthropathy: steroid injections and radiofrequency ablation. Tech Vasc Interv Radiol 12(1):22–32

    PubMed  Google Scholar 

  8. Kallewaard JW, Terheggen MA, Groen GJ, Sluijter ME, Derby R, Kapural L et al (2010) 15. Discogenic low back pain. Pain Practice 10(6):560–579

    PubMed  Google Scholar 

  9. Gunzburg R, Fraser RD, Fraser GA (1990) Lumbar intervertebral disc prolapse in teenage twins. A case report and review of the literature. The Journal of bone and joint surgery British 72(5):914–916

    CAS  Google Scholar 

  10. Barton PM (1991) Piriformis syndrome: a rational approach to management. Pain 47(3):345–352

    CAS  PubMed  Google Scholar 

  11. Zheng Z, Wang J, Gao Q, Hou J, Ma L, Jiang C, Chen G (2012) Therapeutic evaluation of lumbar tender point deep massage for chronic non-specific low back pain. J Tradit Chin Med 32(4):534–537

    PubMed  Google Scholar 

  12. O’Sullivan PB, Beales DJ, Beetham JA, Cripps J, Graf F, Lin IB, Tucker B, Avery A (2002) Altered motor control strategies in subjects with sacroiliac joint pain during the active straight-leg-raise test. Spine 27(1):E1–E8

    PubMed  Google Scholar 

  13. Bagwell JJ, Bauer L, Gradoz M, Grindstaff TL (2016) The reliability of FABER test hip range of motion measurements. International journal of sports physical therapy 11(7):1101–1105

    PubMed  PubMed Central  Google Scholar 

  14. Shanmugaraj A, Shell JR, Horner NS, Duong A, Simunovic N, Uchida S, Ayeni OR (2018) How useful is the flexion-adduction-internal rotation test for diagnosing femoroacetabular impingement: a systematic review. Clinical journal of sport medicine: official journal of the Canadian Academy of Sport Medicine Publish Ahead of Print

  15. Mayer TG, Tencer AF, Kristoferson SANDRA, Mooney VERT (1984) Use of noninvasive techniques for quantification of spinal range-of-motion in normal subjects and chronic low-back dysfunction patients. Spine 9(6):588–595

    CAS  PubMed  Google Scholar 

  16. Freynhagen R, Baron R (2009) The evaluation of neuropathic components in low back pain. Curr Pain Headache Rep 13(3):185–190

    PubMed  Google Scholar 

  17. Arya RK (2014) Low back pain–signs, symptoms and management. Journal, Indian Academy of Clinical Medicine 15(1):30–41

    Google Scholar 

  18. Gurumoorthi R, Das G, Gupta M, Patil V, Manojkumar S, Mehta P, Ray S (2013) The art of history taking in patient with pain: an ignored but very important component in making diagnosis. Indian Journal of Pain 27(2):59

    Google Scholar 

  19. Bernstein IA, Malik Q, Carville S, Ward S (2017) Low back pain and sciatica: summary of NICE guidance. Bmj 356:i6748

    PubMed  Google Scholar 

  20. Chou R, Qaseem A, Snow V, Casey D, Cross JT, Shekelle P, Owens DK (2007) Diagnosis and treatment of low back pain: a joint clinical practice guideline from the American College of Physicians and the American Pain Society. Ann Intern Med 147(7):478–491

    PubMed  Google Scholar 

  21. Shortliffe EH (1986) Medical expert systems—knowledge tools for physicians. West J Med 145(6):830–839

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. In: Malaysia. Limited, Pearson Education

    Google Scholar 

  23. Shortliffe EH (1974) MYCIN: a rule-based computer program for advising physicians regarding antimicrobial therapy selection (No. AIM-251). STANFORD UNIV CALIF DEPT OF COMPUTER SCIENCE

  24. Weiss SM, Kulikowski CA, Safir A (1977) A model-based consultation system for the long-term management of glaucoma. In IJCAI (Vol. 5, pp. 826-832)

  25. Kahn MG, Ferguson JC, Shortliffe EH, Fagan LM (1985) Representation and use of temporal information in ONCOCIN. In: Proceedings of the Annual Symposium on Computer Application in Medical Care. American Medical Informatics Association, p 172

  26. Miller RA, Pople HE Jr, Myers JD (1982) Internist-I, an experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med 307(8):468–476

    CAS  PubMed  Google Scholar 

  27. Aikins JS, Kunz JC, Shortliffe EH, Fallat RJ (1983) PUFF: an expert system for interpretation of pulmonary function data. Comput Biomed Res 16(3):199–208

    CAS  PubMed  Google Scholar 

  28. Seto E, Leonard KJ, Cafazzo JA, Barnsley J, Masino C, Ross HJ (2012) Developing healthcare rule-based expert systems: case study of a heart failure telemonitoring system. Int J Med Inform 81(8):556–565

    PubMed  Google Scholar 

  29. Naser SSA, Akilla AN (2008) A proposed expert system for skin diseases diagnosis. J Appl Sci Res 4(12):1682–1693

    Google Scholar 

  30. Dhanaseelan R, Sutha MJ (2018) Diagnosis of coronary artery disease using an efficient hash table based closed frequent itemsets mining. Medical & biological engineering & computing 56(5):749–759

    Google Scholar 

  31. Dao TT (2018) From deep learning to transfer learning for the prediction of skeletal muscle forces. Medical & biological engineering & computing:1–10

  32. Khattak MT, Supriyanto E, Aman MN, Al-Ashwal RH (2019) Predicting Down syndrome and neural tube defects using basic risk factors. Medical & biological engineering & computing:1–8

  33. Wang Q, Zhao D, Wang Y, Hou X (2019) Ensemble learning algorithm based on multi-parameters for sleep staging. Medical & biological engineering & computing:1–15

  34. Lin L, Hu PJH, Sheng ORL (2006) A decision support system for lower back pain diagnosis: uncertainty management and clinical evaluations. Decis Support Syst 42(2):1152–1169

    Google Scholar 

  35. Sari M, Gulbandilar E, Cimbiz A (2012) Prediction of low back pain with two expert systems. J Med Syst 36(3):1523–1527

    PubMed  Google Scholar 

  36. Kadhim MA, Alam MA, Kaur H (2011) Design and implementation of fuzzy expert system for back pain diagnosis. Int J Innov Technol Creative Eng 1(9):16–22

    Google Scholar 

  37. Toth-Tascau M, Stoia DI, Andrei D (2012) Integrated methodology for a future expert system used in low back pain management. In: 2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, pp 315–320

  38. Abu-Naser, S. S., & ALDAHDOOH, R. (2016). Lower back pain expert system diagnosis and treatment

    Google Scholar 

  39. Zhang NL, Poole D (1996) Exploiting causal independence in Bayesian network inference. J Artif Intell Res 5:301–328

    Google Scholar 

  40. Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 29(3):31–44

    Google Scholar 

  41. https://rstudio-pubs-static.s3.amazonaws.com/270798_46c7a5a8fb7d4980af1b942443271a6f.html. Accessed on November 04, 2019

  42. https://www.kaggle.com/sammy123/lower-back-pain-symptoms-dataset. Accessed on October 30, 2019

  43. Guzmán J, Esmail R, Karjalainen K, Malmivaara A, Irvin E, Bombardier C (2001) Multidisciplinary rehabilitation for chronic low back pain: systematic review. Bmj 322(7301):1511–1516

    PubMed  PubMed Central  Google Scholar 

  44. Waddell G, Feder G, Lewis M (1997) Systematic reviews of bed rest and advice to stay active for acute low back pain. Br J Gen Pract 47(423):647–652

    CAS  PubMed  PubMed Central  Google Scholar 

  45. van Tulder MW, Ostelo R, Vlaeyen JW, Linton SJ, Morley SJ, Assendelft WJ (2000) Behavioral treatment for chronic low back pain: a systematic review within the framework of the Cochrane back review group. Spine 25(20):2688–2699

    PubMed  Google Scholar 

  46. van Tulder MW, Koes BW, Bouter LM (1997) Conservative treatment of acute and chronic nonspecific low back pain: a systematic review of randomized controlled trials of the most common interventions. Spine 22(18):2128–2156

    PubMed  Google Scholar 

  47. Pauza KJ, Howell S, Dreyfuss P, Peloza JH, Dawson K, Bogduk N (2004) A randomized, placebo-controlled trial of intradiscal electrothermal therapy for the treatment of discogenic low back pain. Spine J 4(1):27–35

    PubMed  Google Scholar 

  48. Licciardone JC, Brimhall AK, King LN (2005) Osteopathic manipulative treatment for low back pain: a systematic review and meta-analysis of randomized controlled trials. BMC Musculoskelet Disord 6(1):43

    PubMed  PubMed Central  Google Scholar 

  49. Searle A, Spink M, Ho A, Chuter V (2015) Exercise interventions for the treatment of chronic low back pain: a systematic review and meta-analysis of randomised controlled trials. Clin Rehabil 29(12):1155–1167

    PubMed  Google Scholar 

  50. Magalhaes FN, Dotta L, Sasse A, Teixeira MJ, Fonoff ET (2012) Ozone therapy as a treatment for low back pain secondary to herniated disc: a systematic review and meta-analysis of randomized controlled trials. Pain Physician

  51. Koes BW, van Tulder M, Thomas S (2006) Diagnosis and treatment of low back pain. Bmj 332(7555):1430–1434

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Thornbury JR, Fryback DG, Turski PA, Javid MJ, McDonald JV, Beinlich BR, Gentry LR, Sackett JF, Dasbach EJ, Martin PA (1993) Disk-caused nerve compression in patients with acute low-back pain: diagnosis with MR, CT myelography, and plain CT. Radiology 186(3):731–738

    CAS  PubMed  Google Scholar 

  53. Patel AT, Ogle AA (2000) Diagnosis and management of acute low back pain. Am Fam Physician 61(6):1779–1786

    CAS  PubMed  Google Scholar 

  54. Vanneman ME, Larson MJ, Chen C, Adams RS, Williams TV, Meerwijk E, Harris AH (2018) Treatment of low back pain with opioids and nonpharmacologic treatment modalities for Army veterans. Med Care 56(10):855–861

    PubMed  PubMed Central  Google Scholar 

  55. Traeger A, Buchbinder R, Harris I, Maher C (2017) Diagnosis and management of low-back pain in primary care. Cmaj 189(45):E1386–E1395

    PubMed  PubMed Central  Google Scholar 

  56. Urits I, Burshtein A, Sharma M, Testa L, Gold PA, Orhurhu V, Viswanath O, Jones MR, Sidransky MA, Spektor B, Kaye AD (2019) Low back pain, a comprehensive review: pathophysiology, diagnosis, and treatment. Curr Pain Headache Rep 23(3):23

    PubMed  Google Scholar 

  57. Suzuki H, Kanchiku T, Imajo Y, Yoshida Y, Nishida N, Taguchi T (2016) Diagnosis and characters of non-specific low back pain in Japan: the Yamaguchi low back pain study. PLoS One 11(8):e0160454

    PubMed  PubMed Central  Google Scholar 

  58. Santra D, Basu SK, Mandal JK, Goswami S (2020) Rough set based lattice structure for knowledge representation in medical expert systems: low back pain management case study. Expert Syst Appl 145:113084

    Google Scholar 

  59. Kong G, Xu DL, Yang JB (2008) Clinical decision support systems: a review on knowledge representation and inference under uncertainties. International Journal of Computational Intelligence Systems 1(2):159–167

    Google Scholar 

  60. Iqbal K, Yin XC, Hao HW, Ilyas QM, Ali H (2015) An overview of bayesian network applications in uncertain domains. International Journal of Computer Theory and Engineering 7(6):416–427

    Google Scholar 

  61. Jackson, A., Kuivenhoven, A., & Webster, M. N. (1994). EHL test machine for measuring lubricant film thickness and traction.U.S. Patent No. 5,372,033. Washington, DC: U.S. Patent and Trademark Office

  62. http://mathworld.wolfram.com/RelativeError.html. Accessed on November 20, 2019

  63. https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51. Accessed on November 12, 2019

  64. Using Chi-Square Statistic in Research - Statistics Solutions. https://www.statisticssolutions.com/using-chi-square-statistic-in-research/. Accessed 30 Apr 2019

  65. https://blog.exsilio.com/all/accuracy-precision-recall-f1-score-interpretation-of-performance-measures/.Accessed on November 14, 2019

  66. https://towardsdatascience.com/inferential-statistic-understanding-hypothesis-testing-using-chi-square-test-eacf9fcac533. Accessed on December 01, 2019

Download references

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.

Author information

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

Authors
  1. Debarpita Santra
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jyotsna Kumar Mandal
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Swapan Kumar Basu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Subrata Goswami
    View author publications

    You can also search for this author inPubMed Google Scholar

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.

Corresponding author

Correspondence to Debarpita Santra.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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.

Statement of human rights

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.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-020-02222-9

Keywords