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Exploring a role for MCRDR in enhancing telehealth diagnostics

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Abstract

In-home telehealth devices are becoming increasingly popular when it comes to supporting the health management of home-based patients. With the devices capable of highly active monitoring, using sensors which produce large amounts of data, the deployment of telehealth devices into the home highlights the need for improved ways to collate, classify and dynamically interpret data safely and effectively. For clinicians working at a distance, the amounts of data generated by all in-home patient telematics devices poses questions on how best to intelligently filter, analyze and interpret this data to make diagnoses and respond to changes in patient conditions. In order to manage this issue, expert systems, applied for decades in other health fields, might play a role. In this paper, we explore how one type of expert system, Multiple Classification Ripple Down Rules (MCRDR), might address the issues. This paper begins by reviewing the capabilities of expert systems. Specifically, MCRDR is reviewed and its integration with an example telehealth device, MediStation, is explored. The range of potential benefits which might accrue when MCRDR and the MediStation are linked is identified as are some research and development challenges. Moving forwards, a simple simulator is introduced as one approach which is shown to be effective at exploring this exciting area of research. This paper takes the first steps towards introducing expert systems into the uHealth field and presents a simulator for this purpose.

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References

  1. Bindoff I, Tenni P, Kang BH, Peterson G (2006) Intelligent decision support for medication review. In: Advances in knowledge acquisition and management. Springer, pp 120–131

  2. Bindoff I, Kang BH, Ling T, Tenni P, Peterson G (2007) Applying mcrdr to a multidisciplinary domain. In: AI 2007: advances in artificial intelligence. Springer, pp 519–528

  3. Cagalaban G, Soh W, Kim S (2011) Devising an optimal scheme for wireless sensors for patient report tracking and monitoring in ubiquitous healthcare. IJSEIA 5(4):63–76

    Google Scholar 

  4. Caytiles RD, Park S (2012) u-healthcare: the next healthcare service paradigm. International Journal of Bio-Science and Bio-Technology 4:77–82

    Google Scholar 

  5. Compton P (1989) Maintaining an expert system. In: Application of expert systems, pp 366–385

  6. Compton P, Jansen R (1990) A philosophical basis for knowledge acquisition. Knowl Acquis 2(3):241–258

    Article  Google Scholar 

  7. Compton P, Peters L, Edwards G, Lavers TG (2006) Experience with ripple-down rules. Knowl-Based Syst 19(5):356–362

    Article  Google Scholar 

  8. Edwards G, Compton P, Malor R, Srinivasan A, Lazarus L (1993) Peirs: a pathologist-maintained expert system for the interpretation of chemical pathology reports. Pathology 25(1):27–34

    Article  Google Scholar 

  9. Han SC, Mirowski L, Jeon SH, Lee GS, Kang BH, Turner P (2013) Home-based telehealth devices and the national broadband network (NBN): reflections on technical feasibility versus patient need. In: Proceedings international conference, UCMA, SIA, CCSC, ACIT 2013, 23–25 May 2013, Xian, China, pp 67–72

  10. Han SC, Yoon HG, Kang BH, Park SB (2013) Using mcrdr based agile approach for expert system development. Computing 1–12

  11. Jang-Jae Lee JJL, Byuong-Ho Song BHS, Tae-Yeun Kim TYK, Dae-Woong Seo DWS, Sang-Hyun Bae SHB (2008) A design and implementation of u-health diagnosis system using expert system and neural network. International Journal of Future Generation Communication and Networking 1(1):83–90

    Google Scholar 

  12. Kang B, Compton P, Preston P (1995) Multiple classification ripple down rules: evaluation and possibilities. In: 9th AAAI-sponsored Banff knowledge acquisition for knowledge-based systems workshop, Banff, Canada. University of Calgary

  13. Kim YS, Park SS, Deards E, Kang BH (2004) Adaptive web document classification with mcrdr. In: International conference on information technology: coding and computing, 2004. Proceedings. ITCC 2004, vol 1. IEEE, pp 476–480

  14. Ledley RS, Lusted LB (1959) Reasoning foundations of medical diagnosis. Science 130(3366):9–21

    Article  Google Scholar 

  15. Liao SH (2005) Expert system methodologies and applications a decade review from 1995 to 2004. Expert Syst Appl 28(1):93–103

    Article  Google Scholar 

  16. Ling T, Kang BH, Johns DP, Walls J, Bindoff I (2008) Expert-driven knowledge discovery. In: Fifth international conference on information technology: new generations, 2008. ITNG 2008. IEEE, pp 174–178

  17. Linner T, Ellmann B, Bock T (2011) Ubiquitous life support systems for an ageing society in japan. In: Ambient assisted living. Springer, pp 31–48

  18. Marien M (2002) Futures studies in the 21st century: a reality-based view. Futures 34(3):261–281

    Article  Google Scholar 

  19. Miranda-Mena TG, Benítez U SL, Ochoa JL, Martínez-Béjar R, Fernández-Breis JT, Salinas J (2006) A knowledge-based approach to assign breast cancer treatments in oncology units. Expert Syst Appl 31(3):451–457

    Article  Google Scholar 

  20. Prochaska JO, Velicer WF, Rossi JS, Redding CA, Greene GW, Rossi SR, Sun X, Fava JL, Laforge R, Plummer BA, et al (2004) Multiple risk expert systems interventions: impact of simultaneous stage-matched expert system interventions for smoking, high-fat diet, and sun exposure in a population of parents. Health Psychol 23(5):503–516

    Article  Google Scholar 

  21. Prochaska JO, Velicer WF, Redding C, Rossi JS, Goldstein M, DePue J, Greene GW, Rossi SR, Sun X, Fava JL, et al (2005) Stage-based expert systems to guide a population of primary care patients to quit smoking, eat healthier, prevent skin cancer, and receive regular mammograms. Prev Med 41(2):406–416

    Article  Google Scholar 

  22. Shortliffe EH, Buchanan BG, Feigenbaum EA (1979) Knowledge engineering for medical decision making: a review of computer-based clinical decision aids. Proc IEEE 67(9):1207–1224

    Article  Google Scholar 

  23. Tolentino RS, Park S (2010) A study on u-healthcare system for patient information management over ubiquitous medical sensor networks. IJAST 18:1–10

    Google Scholar 

  24. Vazey M, Richards D (2005) Troubleshooting at the call centre: a knowledge-based approach. In: Proceedings of the artificial intelligence and applications

  25. Velicer WF, Prochaska JO, Bellis JM, DiClemente CC, Rossi JS, Fava JL, Steiger JH (1993) An expert system intervention for smoking cessation. Addict Behav 18(3):269–290

    Article  Google Scholar 

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Acknowledgements

This project was funded by Korea Small and Medium Business Administration.

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Authors and Affiliations

  1. School of Computing and Information System, University of Tasmania, Hobart, Australia

    Soyeon Caren Han, Luke Mirowski & Byeong Ho Kang

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  1. Soyeon Caren Han
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  2. Luke Mirowski
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  3. Byeong Ho Kang
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Correspondence to Soyeon Caren Han.

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Han, S.C., Mirowski, L. & Kang, B.H. Exploring a role for MCRDR in enhancing telehealth diagnostics. Multimed Tools Appl 74, 8467–8481 (2015). https://doi.org/10.1007/s11042-013-1613-7

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