Computer aided fuzzy medical diagnosis.

Centre for Computational Intelligence, Department of Computer Science, De Montfort University, The Gateway, Leicester LE1 9BH, UK
Inf. Sci 01/2004; 162:81-104. DOI: 10.1016/j.ins.2004.03.003
Source: DBLP

ABSTRACT This paper describes a fuzzy approach to computer aided medical diagnosis in a clinical context. It introduces a formal view of diagnosis in clinical settings and shows the relevance and possible uses of fuzzy cognitive maps and fuzzy logic. A constraint satisfaction method is introduced which uses the temporal uncertainty in symptom durations that may occur with particular diseases. Together with fuzzy symptom descriptions, the method results in an estimate of the stage of a disease if the temporal constraints of the disease in relation to the occurrence of the symptoms are satisfied. The approach is evaluated through simulation experiments showing the effects of symptom ordering, temporal uncertainty and symptom strengths on the diagnosis efficiency. The method is effective and can be developed further using second order (Type 2) fuzzy logic to better represent uncertainty in the clinical context thus improving differential diagnosis accuracy.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We present a new model for structuring clinical data in primary care, denoted by the observation space. This model is intended to be used within Electronic Medical Record (EMR), and is designed to meet the requirements of clinical contexts and nuances that characterize primary care practices. While these contexts are generally captured with free-text descriptions, structuring them makes the use of EMR very attractive, since it opens many possibilities such as clinical data exchange and the design of Clinical Decision Support Systems. In partnership with practitioners, the results presented in this paper are being used to build clinical patterns.
    eTELEMED 2013, The Fifth International Conference on eHealth, Telemedicine, and Social Medicine; 02/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The paper proposes an approach to support human abductive reasoning in the diagnosis of a multiviewpoint system. The novelty of this work lies on the capability of the approach to treat the uncertainty held by the agent performing the diagnosis. To do so, we make use of evidential networks to represent and propagate the uncertain evidence gathered by the agent. Using forward and backward propagation of the information, the impact of the evidence over the different symptoms and causes of failure is quantified. The agent can then make use of this information as additional hints in an iterative diagnosis process until a desired degree of certainty is obtained. The model is compared with a deterministic one in which evidence is represented by binary states, that is, a symptom is either observed or not.
    Information Sciences 12/2013; 253:110-125. · 3.64 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper I consider the much mulled over question of whether vagueness is an exclusively linguistic phenomenon, or whether there are actually things in the world that are intrinsically vague. I argue that vagueness affects our descriptions of real world objects in several different ways. It not only affects the identification of objects as being examples of some class, but also the individuation and demarcation criteria of objects. I present a formal semantics that models indeterminacy in both predicates and objects. A vague object is taken to be a referent of a singular term or variable, whose identity is fixed, but whose exact demarcation and constituents are indeterminate.