TY - GEN
T1 - An application of time-changing feature selection
AU - Zhang, Yihao
AU - Orgun, Mehmet A.
AU - Lin, Weiqiang
AU - Graco, Warwick
PY - 2006
Y1 - 2006
N2 - This paper describes a time-changing feature selection1 framework based on hierachical distribution method for extracting knowledge from health records. In the framework, we propose three steps for time-changing feature selection. The first step is a qualitative-based search, to find qualitative features (or, structural time-changing features). The second step performs a quantitativebased search, to find quantitative features (or, value time-changing features). In the third step, the results from the first two steps are combined to form hybrid search models to select a subset of global time-changing features according to a certain criterion of medical experts. The present application of the time-changing feature selection method involves time-changing episode history, an integral part of medical health records and it also provides some challenges in time-changing data mining techniques. The application task was to examine time related features of medical treatment services for diabetics. This was approached by clustering patients into groups receiving similar patterns of care and visualising the features devised to highlight interesting patterns of care.
AB - This paper describes a time-changing feature selection1 framework based on hierachical distribution method for extracting knowledge from health records. In the framework, we propose three steps for time-changing feature selection. The first step is a qualitative-based search, to find qualitative features (or, structural time-changing features). The second step performs a quantitativebased search, to find quantitative features (or, value time-changing features). In the third step, the results from the first two steps are combined to form hybrid search models to select a subset of global time-changing features according to a certain criterion of medical experts. The present application of the time-changing feature selection method involves time-changing episode history, an integral part of medical health records and it also provides some challenges in time-changing data mining techniques. The application task was to examine time related features of medical treatment services for diabetics. This was approached by clustering patients into groups receiving similar patterns of care and visualising the features devised to highlight interesting patterns of care.
UR - http://www.scopus.com/inward/record.url?scp=37149008887&partnerID=8YFLogxK
M3 - Conference proceeding contribution
AN - SCOPUS:37149008887
SN - 3540325476
SN - 9783540325475
VL - 3755 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 203
EP - 217
BT - Data Mining: Theory, Methodology, Techniques, and Applications
PB - Springer, Springer Nature
CY - Berlin; Heidelberg
ER -