Computer aided fuzzy medical diagnosis

Centre for Computational Intelligence, Department of Computer Science, De Montfort University, The Gateway, Leicester LE1 9BH, UK
Inf. Sci 05/2004; 162(2):81-104. DOI: 10.1016/j.ins.2004.03.003
Source: DBLP


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.

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    • "Iakovidis and Papageorgio (2011) propose to use intuitionistic fuzzy cognitive maps to account for the hesitancy in the doctors' evaluation on the relationship between symptoms and possible diseases. Innocent and John (2004) developed a similar system using type-2 fuzzy sets and they found that accounting for this higher level of imprecision can improve the accuracy of diagnosis. "
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    The 28th Bled eConference #Wellbeing, Bled, Slovenia; 06/2015
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    • "It accepted fuzzy descriptions of a patient's symptoms and inferred fuzzy descriptions of the patient's diseases by means of the fuzzy relationships. Furthermore, several researchers followed Sanchez's [27] approach to extend fuzzy inference to medical engineering, notable examples being Pavlica and Petrovacki [24], Steimann and Adlassnig [28], Innocent and John [15], Palma et al. [23], Seising [29], Quteishat and Lim [25], and Ahn et al. [1]. "
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    Yugoslav journal of operations research 01/2014; 25(00):8-8. DOI:10.2298/YJOR130402008C
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    • "The human diagnosis is based on observations, thus uncertain observations may hinder diagnosis accuracy. Several techniques exist to treat such uncertainty: the Bayesian network based approaches [5] [20] [22] [24], the probability based approaches [7] [17] [18], the subjective evaluation based approaches [41], the belief based approaches [25] [30] [32], the possibility based approaches [6] [12] [14] [15], the evidential network based approaches [3] [19], etc. This paper is about uncertainty treatment using an evidential network for multiviewpoint abductive diagnosis. "
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    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.
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