Abstract
The reliability of the biomedical data plays an essential role in the translation of the computational models and simulations of the human body systems into clinical decision support. Numerical models are commonly linked to the hypotheses on the data range of values due to the lack of in vivo data for some biomaterial variables. However, the reliability of these data is still not fully understood due to a lack of a systematic evaluation approach. The objective of this present study was to assess the reliability of biomedical data using expert judgment and belief theory. A systematic evaluation framework was developed using belief theory to perform the expert elicitation process. Seven parameters related to the muscle morphology and mechanics and motion analysis were selected. Twenty data sources related to these parameters were acquired using a systematic review process on the reliable search engines. A questionnaire was established including four main questions and four complementary questions related to the confidence levels. Eleven experts participated into the evaluation process via Google Form. A transformation process was developed to convert qualitative expert judgments to the numeric representations of the mass functions in the framework of belief theory. Two combination rules (Demspter and Dubois-Prade) were used to fuse the responses of multiple experts. At the end, data reliability was assessed using the pignistic probability to select the sources that correspond to some on-demand levels of confidence.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aboal, J.R., Boquete, M.T., Carballeira, A., Casanova, A., Fernández, J.A.: Quantification of the overall measurement uncertainty associated with the passive moss biomonitoring technique: sample collection and processing. Environ. Pollut. 224, 235–242 (2017)
Boone, I., Van der Stede, Y., Bollaerts, K., Messens, W., Mintiens, K.: Expert judgement in a risk assessment model for Salmonella spp. in pork: the performance of different weighting schemes. Prev. Vet. Med. 92(3), 224–234 (2009)
Charles Osuagwu, C., Okafor, E.C.: Framework for eliciting knowledge for a medical laboratory diagnostic expert system. Expert Syst. Appl. 37(7), 5009–5016 (2010)
Chatterjee, S., Bhattacharyya, M.: Judgment analysis of crowdsourced opinions using biclustering. Inf. Sci. 375(1), 138–154 (2017)
Cobb, J.B.R., Shenoy, P.P.: On the plausibility transformation method for translating belief function models to probability models. Int. J. Approx. Reason. 41(3), 314–330 (2006)
Dubois D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Comput. Intell. (1988)
Hanea, D.M., Jagtman, H.M., van Alphen, L.L.M.M., Ale, B.J.M.: Quantitative and qualitative analysis of the expert and non-expert opinion in fire risk in buildings. Reliab. Eng. Syst. Saf. 95(7), 729–741 (2010)
Jörg, E., Julia, H., Valentin, Q., Markus, T., Björn, R.: Biomechanical model based evaluation of Total Hip Arthroplasty therapy outcome. J. Orthop. 14(4), 582–588 (2017)
Lev, V.U.: A method for processing the unreliable expert judgments about parameters of probability distributions. Eur. J. Oper. Res. 175(1), 385–398 (2006)
Nicholas, T., Danielle, P., Nikhil, V.D., Robert, P.L.: Biomechanical analysis of gait waveform data: exploring differences between shod and barefoot running in habitually shod runners. Gait Posture 58, 274–279 (2017)
Nicolas, R., Didier, P., Julie, C., Johanna, R., Raphael, Z.: Categorization of gait patterns in adults with cerebral palsy: a clustering approach. Gait Posture 39(1), 235–240 (2014)
Pauk, J., Minta-Bielecka, K.: Gait patterns classification based on cluster and bicluster analysis. Biocybern. Biomed. Eng. 36(2), 391–396 (2016)
Rustem, B., Robin Becker, G., Wolfgang, M.: Robust min–max portfolio strategies for rival forecast and risk scenarios. J. Econ. Dyn. Control 24(11), 1591–1621 (2000)
Samuel, T.R., Alejandro, S.: Error correction in multi-fidelity molecular dynamics simulations using functional uncertainty quantification. J. Comput. Phys. 334(1), 207–220 (2017)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976)
Skinner, D.J.C., Rocks, S.A., Pollard, S.J.T.: Where do uncertainties reside within environmental risk assessments? Expert opinion on uncertainty distributions for pesticide risks to surface water organisms. Sci. Total Environ. 572, 23–33
Smets, P.: Data fusion in the transferable belief model. In: Proceedings of 3rd International Conference on Information Fusion, Paris, France, pp. 21–33 (2000)
Smets, P.: Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. Int. J. Approx. Reason. 9(1), 1–35 (1993)
Wang, P., Ma, Z., Tian, Y.: Application of expert judgment method in the aircraft wiring risk assessment. Proc. Eng. 17, 440–445 (2011)
Yun, Z., Norman, F., Martin, N.: Bayesian network approach to multinomial parameter learning using data and expert judgments. Int. J. Approx. Reason. 55(5), 1252–1268 (2014)
Acknowledgements
The authors would like to thank all anonymous experts participating into the evaluation process.
Funding
This work was carried out and funded in the framework of the Labex MS2T. It was supported by the French Government, through the program “Investments for the future” managed by the National Agency for Research (Reference ANR-11-IDEX-0004-02).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Hoang, T.N., Dao, T.T., Ho Ba Tho, MC. (2018). A Method for Uncertainty Elicitation of Experts Using Belief Function. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-76081-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76080-3
Online ISBN: 978-3-319-76081-0
eBook Packages: EngineeringEngineering (R0)