A Temporal Data Mining Approach for Discovering Knowledge on the Changes of the Patient’s Physiology
Physiological data represent the health conditions of a patient over time. They can be analyzed to gain knowledge on the course of a disease or, more generally, on the physiology of a patient. Typical approaches rely on background medical knowledge to track or recognize single stages of the disease. However, when no one domain knowledge is available these approaches become inapplicable. In this paper we describe a Temporal Data Mining approach to acquire knowledge about the possible causes which can trigger particular stages of the disease or, more generally, which can determine changes in the patient’s physiology. The analysis is performed in two steps: first, identification of the states of the disease (namely, the stages through which the physiology evolves), then detection of the events which may determine the change from a state to the next one. Computational solutions to both issues are presented. The application to the scenario of the sleep disorders allows to discover events, in the form of breathing and cardiovascular disorders, which may trigger particular sleep stages. Results are evaluated and discussed.
KeywordsTemporal Data Mining Physiological Data States Events
Unable to display preview. Download preview PDF.
- 1.Adlassnig, K.P.: Fuzzy Systems in Medicine. In: Proc. of the International Conference in Fuzzy Logic and Technology, pp. 2–5 (2001)Google Scholar
- 2.Fawcett, T., Provost, F.: Activity Monitoring: Noticing Interesting Changes in Behavior. In: KDD 1999, pp. 53–62 (1999)Google Scholar
- 3.Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23), e215–e220 (2000)CrossRefGoogle Scholar
- 5.Guyet, T., Garbay, C., Dojat, M.: Human/Computer Interaction to Learn Scenarios from ICU Multivariate Time Series. In: AIME, pp. 424–428 (2005)Google Scholar
- 8.Lavrac, N., Zupan, B.: Data Mining in Medicine. In: The Data Mining and Knowledge Discovery Handbook, pp. 1107–1138 (2005)Google Scholar
- 9.Loglisci, C., Berardi, M.: Segmentation of Evolving Complex Data and Generation of Models. In: ICDM Workshops, pp. 269–273 (2006)Google Scholar
- 10.Malerba, D.: Learning Recursive Theories in the Normal ILP Setting. Fundam. Inf. 57(1), 39–77 (2003)Google Scholar