Using a knowledge-integration model to construct a recommendation system for matching outpatient symptoms and hospital clinical departments

Abstract

Knowledge categorization is the shared recognition of people to a certain knowledge domain. It serves the purposes such as description, interpretation, communication, and inference of a domain of knowledge. Because the application of knowledge in practice for problem-solving can involve multiple domains, therefore how to construct logic relationships among the knowledge categorizations is often the key to problem-solving. This study takes the case of outpatients’ perception of signs and symptoms as an example domain to explore how knowledge categorization and integration could help outpatients query and choose from the departments of a healthcare institute at registration. This study uses ontology modeling to develop the needed knowledge categorization and problem-solving models. The major contents include: (1) Clarifying the content of the knowledge sources, including the knowledge categories of symptoms and illness; (2) Constructing a domain ontology of general knowledge sources consisting of common conceptual structure and instances to provide reference standards or terminology when communicating with other domains; (3) Establishing an objective-oriented task-ontology by developing the relationships and logic among the knowledge sources in accordance with the needs of problem-solving, and then collecting existing facts as instance knowledge in accordance with the knowledge schema of the concepts; and (4) Developing a set of inferable semantic rules for problem-solving to infer the implicit knowledge based on the aforementioned factual knowledge. The experiment results show that the procedures of knowledge categorization and integration developed in this study, with the modeling of domain ontology, task ontology, and inference rules, have preliminarily achieved the purpose of solving the problem of matching outpatients’ signs and symptoms with the suitable hospital department. Furthermore, the results of this study have simplified the future maintenance and expansion of the domain content knowledge and thus enabled effective knowledge integration.

Department(s)

Business and Information Technology

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 The Authors, All rights reserved.

Publication Date

2013

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