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Advances in the Decision Making for Treatments of Chronic Patients Using Fuzzy Logic and Data Mining Techniques

 

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Author: Domínguez Muñz, Manuel
Aroba, J.
González Enríquez, José
Ramos Román, Isabel
Lucena Soto, José Manuel
Escalona Cuaresma, María José
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2014
Published in: ICEIS 2014: 16th International Conference on Enterprise Information Systems (2014), p 325-330
ISBN/ISSN: 978-989-758-027-7
Document type: Presentation
Abstract: Virological events in HIV-infected patients can rise with no apparent reason. Therefore, when they appear, immunologists or medical doctors do not know whether they will produce other future virological events or they will entail relevant clinical consequences. This paper presents the results of applying Prefurge to HIV- infected patients’ clinical data, with the aim of obtaining rules and information about this set of clinical trials data that will relate these kinds of virological events.
Cite: Domínguez Muñz, M., Aroba, J., González Enríquez, J., Ramos Román, I., Lucena Soto, J.M. y Escalona Cuaresma, M.J. (2014). Advances in the Decision Making for Treatments of Chronic Patients Using Fuzzy Logic and Data Mining Techniques. En ICEIS 2014: 16th International Conference on Enterprise Information Systems (325-330), Lisboa, Portugal: ScitePress Digital Library.
Size: 389.1Kb
Format: PDF

URI: http://hdl.handle.net/11441/59099

DOI: 10.5220/0004969503250330

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