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CASE‐BASED REASONING AND KNOWLEDGE DISCOVERY IN MEDICAL APPLICATIONS WITH TIME SERIES
Author(s) -
Funk Peter,
Xiong Ning
Publication year - 2006
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2006.00286.x
Subject(s) - computer science , key (lock) , artificial intelligence , case based reasoning , task (project management) , knowledge extraction , process (computing) , machine learning , data mining , natural language processing , information retrieval , computer security , management , economics , operating system
This paper discusses the role and integration of knowledge discovery (KD) in case‐based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case‐specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by a real application scenario in medicine in which time series of patterns are to be analyzed and classified. As single pattern cannot convey sufficient information in the application, sequences of patterns are more adequate. Hence it is advantageous if sequences of patterns and their co‐occurrence with categories can be discovered. Evaluation with cases containing series classified into a number of categories and injected with indicator sequences shows that the approach is able to identify these key sequences. In a clinical applica‐
tion and a case library that is representative of the real world, these key sequences would improve the classification ability and may spawn clinical research to explain the co‐occurrence between certain sequences and classes.