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

Autor:	Domínguez Muñoz, Manuel, Aroba Páez, Javier, González Enríquez, José, Ramos Román, Isabel, Lucena Soto, José Manuel, Escalona Cuaresma, María José
Přispěvatelé:	Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla. TIC021: Ingeniería Web y Testing Temprano (IWT2), Junta de Andalucía
Rok vydání:	2014
Předmět:	Prefurge HIV Unsupervised Learning Virological Events
Zdroj:	idUS. Depósito de Investigación de la Universidad de Sevilla instname
Popis:	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. Junta de Andalucía TIC-5789
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a62119a276822396c65fda8cfe6d79d7 https://idus.us.es/handle/11441/59099 Zobrazit plný text záznamu