Information Technology Reference
In-Depth Information
issues, we have used a semiotic approach to modeling of medical concepts. Semi-
otics provides the modeling constructs for the description of the concept, its rep-
resentation, interpretation, and utilization. Furthermore, we have observed that (1)
data mining requires explicit conceptual models based on operational definitions of
medical concepts; and (2) the quality of the data-mining process depends on the
quality of the conceptual data models and the quality of the data.
We are planning to further expand the proposed framework and to build a com-
prehensive computational model for the medical concept of obstructive sleep apnea
and its symptoms. We will apply this model in a clinical decision support system
for the diagnosis and treatment of
OSA
, as well as in a support system for the treat-
ment of sleep disorders. Furthermore, we plan to utilize the proposed computational
model for the analysis of patients' data from clinics which use diverse diagnostic cri-
teria. The explicit modeling will allow us to compare treatment results from various
clinics.
References
1. Bruner, J.S., Goodnow, J.J., Austin, G.A.: A Study of Thinking. Wiley, New York (1956)
2. Chandler, D.: Semiotics: The Basics. Routledge, London (2002)
3. Curcio, G., Casagrande, M., Bertini, M.: Sleepiness: Evaluating and Quantifying Meth-
ods. International Journal of Psychophysiology 41, 251-263 (2001)
4. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discov-
ery: An Overview. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.
(eds.) Advances in Knowledge Discovery and Data Mining, pp. 37-54. The MIT Press,
Menlo Park (1996)
5. Ferguson, K.A., Ono, T., Lowe, A.A., Ryan, C.F., Fleetham, J.A.: The Relationship
Between Obesity and Craniofacial Structure in Obstructive Sleep Apnea. Chest 108(2),
375-381 (1995)
6. Goble, C.A., Glowinski, A.J., Nowlan, W.A., Rector, A.L.: A Descriptive Semantic
Formalism for Medicine. In: Proceedings of the Ninth International Conference on Data
Engineering, pp. 624-631 (1993)
7. Kwiatkowska, M., Ayas, N.T., Ryan, C.F.: Evaluation of Clinical Prediction Rules
Using a Convergence of Knowledge-driven and Data-driven Methods: A Semio-fuzzy
Approach. In: Zanasi, A., Brebbia, C.A., Ebecken, N.F.F. (eds.) Data Mining VI:
Data Mining, Text Mining and their Business Applications, pp. 411-420. WIT Press,
Southampton (2005)
8. Kwiatkowska, M., Atkins, M.S., Rollans, S., Ryan, C.F., Ayas, N.T.: Decision Tree
Induction in the Creation of Prediction Models for Obstructive Sleep Apnea (
OSA
): A
Pilot Study (Abstract). In: International Conference of American Thoracic Society, San
Diego (2006)
9. Lam, B., Ip, M.S.M., Tench, E., Frank Ryan, C.: Craniofacial Profile in Asian and white
Subjects with Obstructive Sleep Apnea. Thorax 60(6), 504-510 (2005)
10. Medin, D.L., Marguerite, M., Medin, D.L., Schaffer, M.M.: Context Theory of Classifi-
cation Learning. Psychological Review 85, 207-238 (1978)
11. Minda, J.P., David Smith, J.: The Effects of Category Size, Category Structure and Stim-
ulus Complexity. Journal of Experimental Psychology: Learning, Memory and Cogni-
tion 27, 755-799 (2001)
