Skip to main content
SpringerLink
Log in

Viterbi Algorithm and HMM Implementation to Multicriteria Data-Driven Decision Support Model for Optimization of Medical Service Quality Selection

  • Conference paper
  • First Online:
Advanced Information Networking and Applications (AINA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 450))

  • 859 Accesses

Abstract

Medical service quality is one of the major factors upon which Quality of Life Index is calculated. In this paper, we propose a Multicriteria Data-Driven Decision Support (MDDDS) model for a patient who explores medical database to make a decision on selecting the best medical professional specializing in his or her disease. Optimization of the decision relies on the data-driven collection about the particular disease’s stage and suitability to the efficiency of the medicament recommended by the doctor. Comparison of the medical service quality is based on popularity of the medicament prescribed by other medical professionals and other patients comments suffering from the disease and being ordered the same medicament. Efficiency of the approach proposed has a pragmatic nature such that it can be applied by medical clinics, hospitals and other healthcare institutions as well. We trust that the data collected by our MDDDS model is found of the greatest importance by the patients while seeking a reliable and high-quality healthcare service. Our findings based on the real data simulation indicate that implementation of HMM and Viterbi’s algorithm gives a very promising results in optimization of the patient’s decision process as regardless of the data including the disease entities, the number of the medical service providers, we achieve ambiguous outcome, which in each case allows the patient to make a reliable and firm decision of which doctor is the most successful in treating a particular disease entity.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Keskar, S., Banerjee, R.: Time-recurrent HMM decision tree to generate alerts for heart-guard wearable computer. In: 2011 Computing in Cardiology, pp. 605–608 (2011)

    Google Scholar 

  2. Tai-Guang, G., Min, H., Qing, W., Xing-Wei, W., Pei-You, C.: A multi-issue auto-negotiation system based on HMM. In: 2018 Chinese Control and Decision Conference (CCDC), 2018, pp. 3659–3664 (2018)

    Google Scholar 

  3. Mizera-Pietraszko, J.: Computer-assisted clinical diagnosis in the official European Union languages. In: Proceedings of the 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), Munich, Germany, pp. 1–6. IEEE Computer Society (2016)

    Google Scholar 

  4. Zhou, W., Schlüter, R., Ney, H.: Full-sum decoding for hybrid Hmm based speech recognition using LSTM language model. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7834–7838 (2020)

    Google Scholar 

  5. Kato, T., Kuroiwa, S., Shimizu, T., Higuchl, N.: Efficient mixture Gaussian synthesis for decision tree based state tying. In: 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings (Cat. No.01CH37221), vol. 1, pp. 493–496 (2001)

    Google Scholar 

  6. Wang, Y., Phillips, I.T., Haralick, R.M.: A method for document zone content classification. In: International Conference on Pattern Recognition, vol. 3, pp. 196–199 (2002)

    Google Scholar 

  7. Makonin, S., Popowich, F., Bajić, I.V., Gill, B., Bartram, L.: Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring. IEEE Trans. Smart Grid 7(6), 2575–2585 (2016)

    Google Scholar 

  8. Mizera-Pietraszko, J., Tancula, J.: Rough set theory for supporting decision making on relevance in browsing multilingual digital resources. In: Król, D., Nguyen, N., Shirai, K. (eds.) Advanced Topics in Intelligent Information and Database Systems. ACIIDS 2017. Studies in Computational Intelligence, vol. 710. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54430-4

  9. Palupi, I., Wahyudi, B.A., Putra, A.P.: Implementation of Hidden Markov Model (HMM) to predict financial market regime. In: 2021 9th International Conference on Information and Communication Technology (ICoICT), pp. 639–644 (2021)

    Google Scholar 

  10. Aghasaryan, A., Fabre, E., Benveniste, A., Boubour, R., Jard, C.: A Petri net approach to fault detection and diagnosis in distributed systems. II. Extending Viterbi algorithm and HMM techniques to Petri nets. In: Proceedings of the 36th IEEE Conference on Decision and Control, vol. 1, pp. 726–731 (1997)

    Google Scholar 

  11. Dash, S., Abraham, A., Kr Luhach, A., Mizera-Pietraszko, J., Rodrigues, J.P.C.: Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis. J. Distrib. Sens. Netw. 16(1), 1550147719895210 (2020)

    Google Scholar 

  12. Chien, J.-T., Furui, S.: Predictive Hidden Markov model selection for speech recognition. IEEE Trans. Speech Audio Process. 13(3), 377–387 (2005)

    Article  Google Scholar 

  13. Monkowski, M.D., Picheny, M.A., Srinivasa Rao, P.: Context dependent phonetic duration models for decoding conversational speech. In: 1995 International Conference on Acoustics, Speech, and Signal Processing, pp. 528–531 (1995)

    Google Scholar 

  14. Mizera-Pietraszko, J., Świątek, P.: Access to eHealth language-based services for multinational patients. In: 2015 17th International Conference on E-health Networking, Application & Services (HealthCom), pp. 232–237 (2015)

    Google Scholar 

  15. Gao, F., Sun, J., Wei, Z.: The prediction role of Hidden Markov model in intrusion detection. In: CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436), 2003, vol. 2, pp. 893–896 (2003)

    Google Scholar 

  16. Bu, K., Li, X., Wang, K., Li, Y.: Data analysis of public food safety cases based on Apriori. In: Chinese Control and Decision Conference (CCDC), pp. 343–348 (2020)

    Google Scholar 

  17. Kaltenmeier, A., Caesar, T., Gloger, J.M., Mandler, E.: Sophisticated topology of hidden Markov models for cursive script recognition. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR 1993), pp. 139–142 (1993)

    Google Scholar 

  18. Junkawitsch, J., Neubauer, L., Hoge, H., Ruske, G.: A new keyword spotting algorithm with pre-calculated optimal thresholds. In: Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP 1996, vol. 4, pp. 2067–2070 (1996)

    Google Scholar 

  19. Ozeki, K.: Likelihood normalization using an ergodic HMM for continuous speech recognition. In: Proceeding of Fourth International Conference on Spoken Language Processing, ICSLP 1996, vol. 4, pp. 2301–2304 (1996)

    Google Scholar 

  20. Rathinavelu, C., Deng, L.: The trended HMM with discriminative training for phonetic classification. In: Proceeding of Fourth International Conference on Spoken Language Processing, ICSLP 1996, vol. 2, pp. 1049–1052 (1996)

    Google Scholar 

  21. Yoma, N.B., Villar, M.: Speaker verification in noise using a stochastic version of the weighted Viterbi algorithm. IEEE Trans. Speech Audio Process. 10(3), 158–166 (2002)

    Article  Google Scholar 

  22. Liao, Y.-F., Chen, S.-H.: An MRNN-based method for continuous Mandarin speech recognition. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 (Cat. No. 98CH36181), vol. 2, pp. 1121–1124 (1998)

    Google Scholar 

  23. Hon, H.-W., Wang, K.: Unified frame and segment based models for automatic speech recognition. In: 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 00CH37100), pp. II1017–II1020 (2000)

    Google Scholar 

  24. Basseville, M., Benveniste, A., Tromp, L.: Diagnosing hybrid dynamical systems: fault graphs, statistical residuals and Viterbi algorithms. In: Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No. 98CH36171), vol. 4, pp. 3757–3762 (1998)

    Google Scholar 

  25. Watanabe, Y., Liu, W., Shoji, Y.: Machine-learning-based hazardous spot detection framework by mobile sensing and opportunistic networks. IEEE Trans. Veh. Technol. 69(11), 13646–13657 (2020)

    Article  Google Scholar 

  26. Shah, V., Anstotz, R., Obeid, I., Picone, J.: Adapting an automatic speech recognition system to event classification of electroencephalograms1. In: 2018 IEEE Signal Processing in Medicamente and Biology Symposium (SPMB), pp. 1–5 (2018)

    Google Scholar 

Download references

Acknowledgment

This work was supported in part by a grant from COST Action CA18231 Multi3Generation: Multi-task, Multilingual, Multi-modal Language Generation founded by COST (European Cooperation in Science and Technology).

Author information

Authors and Affiliations

  1. Military University of Land Forces, 51-147, Wroclaw, Poland

    Jolanta Mizera-Pietraszko

  2. Opole University, 45-040, Opole, Poland

    Jolanta Tancula

Authors
  1. Jolanta Mizera-Pietraszko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jolanta Tancula
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jolanta Mizera-Pietraszko .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Technology Sydney, Sydney, NSW, Australia

    Farookh Hussain

  3. Faculty of Bussiness Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mizera-Pietraszko, J., Tancula, J. (2022). Viterbi Algorithm and HMM Implementation to Multicriteria Data-Driven Decision Support Model for Optimization of Medical Service Quality Selection. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_30

Download citation

Publish with us

Policies and ethics

Search

Navigation