Recently, Fuzzy Logic has been proposed as the most suitable approach for profitably tackling uncertainty and vagueness in clinical guidelines, and providing a new mobile generation of Decision Support Systems. This paper presents an intuitive XML-based language, named Fuzzy Decision Support Language, for both configuring a fuzzy inference system and encoding fuzzy medical knowledge to be embedded into a mobile DSS. Such a language enables the encoding of: i) fuzzy medical knowledge, in terms of groups of positive evidence rules and fuzzy ELSE rules assembling all the negative evidence for a specific situation; ii) input and output data, respectively elaborated or produced by the fuzzy DSS, in order to provide meaningful and semantically well-defined advices. As a proof of concept, the proposed language has been applied to encode, into a mobile DSS, the medical knowledge required to remotely detect suspicious situations of sleep apnea or heart failure in patients affected by cardiovascular diseases. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2013.
A fuzzy decision support language for building mobile DSSs for healthcare applications
De Pietro G
2013-01-01
Abstract
Recently, Fuzzy Logic has been proposed as the most suitable approach for profitably tackling uncertainty and vagueness in clinical guidelines, and providing a new mobile generation of Decision Support Systems. This paper presents an intuitive XML-based language, named Fuzzy Decision Support Language, for both configuring a fuzzy inference system and encoding fuzzy medical knowledge to be embedded into a mobile DSS. Such a language enables the encoding of: i) fuzzy medical knowledge, in terms of groups of positive evidence rules and fuzzy ELSE rules assembling all the negative evidence for a specific situation; ii) input and output data, respectively elaborated or produced by the fuzzy DSS, in order to provide meaningful and semantically well-defined advices. As a proof of concept, the proposed language has been applied to encode, into a mobile DSS, the medical knowledge required to remotely detect suspicious situations of sleep apnea or heart failure in patients affected by cardiovascular diseases. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2013.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
