A statistical perspective on knowledge discovery in databases
Advances in knowledge discovery and data mining
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Temporal Data Mining Using Multilevel-Local Polynominal Models
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Temporal Data Mining Using Hidden Periodicity Analysis
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Feature Selection for Temporal Health Records
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Temporal Data Mining Using Hidden Markov-Local Polynomial Models
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
A stratified model for short-term prediction of time series
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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This paper describes a time-changing feature selection 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 quantitative-based 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.