Knowledge discovery about quality of life changes of spinal cord injury patients: clustering based on rules by states.
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.
Gibert
Karina
K
Knowledge Engineering and Machine Learning Group, Department of Statistics and Operations Research, Universitat Politecnica de Catalunya, Barcelona, Spain. karina.gibert@upc.edu
García-Rudolph
Alejandro
A
Curcoll
Lluïsa
L
Soler
Dolors
D
Pla
Laura
L
Tormos
José María
JM
D000328:Adult; D020000:Decision Support Systems, Clinical; D005260:Female; D006801:Humans; D051188:Knowledge Bases; D008297:Male; D008875:Middle Aged; D011788:Quality of Life; D013119:Spinal Cord Injuries / Q000523:psychology*