Knowledge Discovery in Texts for Constructing Decision Support Systems

Authors: Loh S.¹; de Oliveira J.P.M.²; Gameiro M.A.³

Source: Applied Intelligence, Volume 18, Number 3, May 2003 , pp. 357-366(10)

Publisher: Springer

Abstract:

This paper presents a Text Mining approach for discovering knowledge in texts to later construct decision support systems. Text mining can take advantage of knowledge stored in textual documents, reducing the effort for knowledge acquisition. The approach consists in performing a mining process on concepts present in texts instead of working with words. The assumption is that concepts represent real world events and characteristics better than words, allowing the understanding and the explanation of the reasoning used in decision processes. The proposed approach extracts concepts expressed in natural phrases, and then analyzes their distributions and associations. Concepts distributions and associations are used to characterize classes or situations. After the discovery process, the obtained knowledge can be embedded in automated systems to classify elements or to suggest actions or solutions to problems. In this paper, experiments using the approach in a psychiatric domain are discussed. Concepts extracted from textual medical records represent patients' symptoms, signals and social/behavior characteristics. An automatic system was constructed with the approach: a classifier whose goal is to help physicians in disease diagnoses. Results from this system show that the approach is feasible for constructing decision support systems with satisfactory performance.

Keywords: text mining; knowledge discovery; knowledge acquisition; text analysis; decision support systems

Language: English

Document Type: Research article

Affiliations: 1: Instituto de Informática–PPGC, Federal University of Rio Grande do Sul, Porto Alegre, Brazil; Computer Science Department, Catholic University of Pelotas, Pelotas, Brazil; Computer Science Department, Lutheran University of Brazil, Canoas, Brazil. sloh@zaz.com.br 2: Instituto de Informática–PPGC, Federal University of Rio Grande do Sul, Porto Alegre, Brazil. palazzo@inf.ufrgs.br 3: Olivé Leite Hospital, Pelotas, Brazil. magameiro@uol.com.br

Publication date: 2003-05-01

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• In this Subject: Computer Science
• By this author: Loh S. ;  de Oliveira J.P.M. ;  Gameiro M.A.

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