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Knowledge‐based treatment planning for adolescent early intervention of mental healthcare: a hybrid case‐based reasoning approach
Author(s) -
Wang W.M.,
Cheung C.F.,
Lee W.B.,
Kwok S.K.
Publication year - 2007
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2007.00431.x
Subject(s) - computer science , case based reasoning , reasoning system , knowledge base , task (project management) , model based reasoning , construct (python library) , intervention (counseling) , fuzzy logic , deductive reasoning , knowledge management , service (business) , set (abstract data type) , artificial intelligence , management science , knowledge representation and reasoning , medicine , nursing , systems engineering , engineering , economy , economics , programming language
Treatment planning is a crucial and complex task in the social services industry. There is an increasing need for knowledge‐based systems for supporting caseworkers in the decision‐making of treatment planning. This paper presents a hybrid case‐based reasoning approach for building a knowledge‐based treatment planning system for adolescent early intervention of mental healthcare. The hybrid case‐based reasoning approach combines aspects of case‐based reasoning, rule‐based reasoning and fuzzy theory. The knowledge base of case‐based reasoning is a case base of client records consisting of documented experience while that for rule‐based reasoning is a set of IF–THEN rules based on the experience of social service professionals. Fuzzy theory is adopted to deal with the uncertain nature of treatment planning. A prototype system has been implemented in a social services company and its performance is evaluated by a group of caseworkers. The results indicate that hybrid case‐based reasoning has an enhanced performance and the knowledge‐based treatment planning system enables caseworkers to construct more efficient treatment planning in less cost and less time.