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dc.contributor.authorHuang, Yo-Ping
dc.contributor.authorChiu, Hong-Wen
dc.contributor.authorChuan, Wei-Po
dc.contributor.authorSandnes, Frode Eika
dc.date.accessioned2010-11-08T11:48:56Z
dc.date.available2010-11-08T11:48:56Z
dc.date.issued2010
dc.identifier.citationHuang, Y., Chiu, H., Chuan, W. & Sandnes, F.E. (2010). Discovering Fuzzy Association Rules from Patient's Daily Text Messages to Diagnose Melancholia. In: G. Dimirovski (Ed.), Proceedings of SMC2010. Piscataway, N.J. : Institute of Electrical and Electronics Engineersen_US
dc.identifier.isbn978-1-4244-6587-3
dc.identifier.urihttps://hdl.handle.net/10642/497
dc.description.abstractWith the constant stress from work load and daily life people may show symptoms of melancholia. However, most people are reluctant to describe it or may not know that they already have it. In this paper a novel system is proposed to discover clues from patient’s interaction with psychologist or from self-recorded voice or text messages. A user friendly interface is provided for patients to input text messages or record a voice file by mobile phones or other input devices. A speech-totext conversion software is used to convert voice mails to simple text files in advance. Based on the text files, a data mining model is used to discover frequent keywords mentioned in the text or speech files. The association rules can be used to help psychologists diagnose patients’ degree of melancholia. Experimental results show that the proposed system can effectively discover melancholia keywords.en_US
dc.language.isoengen_US
dc.publisherIEEE Pressen_US
dc.subjectData miningen_US
dc.subjectFuzzy modelen_US
dc.subjectWord segmentationen_US
dc.subjectAssociation rulesen_US
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551en_US
dc.subjectVDP::Medisinske Fag: 700::Klinisk medisinske fag: 750::Psykiatri, barnepsykiatri: 757en_US
dc.titleDiscovering Fuzzy Association Rules from Patient's Daily Text Messages to Diagnose Melancholiaen_US
dc.typeChapteren_US
dc.typePeer revieweden_US


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