| Autor: |
Adlassnig, Klaus-Peter, Blobel, Bernd, Mantas, John, Masic, Izet, Gibert, Karina, García-Rudolph, Alejandro, Curcoll, Lluïsa, Soler, Dolors, Pla, Laura, Tormos, José María |
| Zdroj: |
Studies in Health Technology & Informatics; 2009, Vol. 150, p579-583, 5p, 1 Diagram |
| Abstrakt: |
In this paper, an integral Knowledge Discovery Methodology, named Clustering based on rules by States, which incorporates artificial intelligence (AI) and statistical methods as well as interpretation-oriented tools, is used for extracting knowledge patterns about the evolution over time of the Quality of Life (QoL) of patients with Spinal Cord Injury. The methodology incorporates the interaction with experts as a crucial element with the clustering methodology to guarantee usefulness of the results. Four typical patterns are discovered by taking into account prior expert knowledge. Several hypotheses are elaborated about the reasons for psychological distress or decreases in QoL of patients over time. The knowledge discovery from data (KDD) approach turns out, once again, to be a suitable formal framework for handling multidimensional complexity of the health domains. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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