K.P. Adlassnig. Fuzzy set theory in medical diagnosis. IEEE Transactions Systems Man Cybernetics, 16(2):260--265, 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....knowledge [1] In practice, it is important to develop techniques for handling such imprecision and uncertainty to enhance the robustness and performance of medical expert systems. Fuzzy logic and fuzzy set theory [2] provide a good framework for managing uncertainty and imprecision in medicine [3], 4] 5] and have been successfully applied to a number of areas [6] 4] 7] The successful development of a fuzzy model for a particular application domain is a complex multi step process, in which the designer is faced with a large number of alternative implementation strategies. The ....

K.P. Adlassnig, "Fuzzy set theory in medical diagnosis," IEEE Transactions Systems Man Cybernetics, vol. 16, no. 2, pp. 260-- 265, 1986.

....expert knowledge elicitation sessions, and to investigate whether the fuzzy system would offer any improvement in performance over the crisp system in its interpretation of results. Fuzzy logic and fuzzy set theory provide a good framework for managing uncertainty and imprecision in medicine [3, 24, 47] and have been successfully applied to a number of areas [5, 24, 128] However, although the theoretical properties of fuzzy systems have been extensively investigated, the implementation of a fuzzy expert system in practice involves a great deal of pragmatic choices. This includes considerations ....

K.P. Adlassnig. Fuzzy set theory in medical diagnosis. IEEE Transactions Systems Man Cybernetics, 16(2):260--265, 1986.

.... integrated into rule based expert systems with the intent to remedy the brittleness of traditional AI decision support [30, 7] Typically, the source of data was the patient record [12,6,52] and this data was fuzzi ed and processed by a fuzzy inference machine, e.g. by relational calculus [1, 43,41,44,51,50]orby some other heuristic scheme [36,42] The hallmarks of this rst phase are: The main source of data was the patient record. The computational technology was not ecient enough for fuzzy signal or image processing. Most of the fuzzy aid in medicine was linked with AI, and fuzzy sets were ....

K.P. Adlassnig. Fuzzy set theory in medical diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, SMC-16(2):260-265, 1986.

.... integrated into rule based expert systems with the intent to remedy the brittleness of traditional AI decision support [30, 7] Typically, the source of data was the patient record [12, 6, 52] and this data was fuzzi ed and processed by a fuzzy inference machine, e.g. by relational calculus [1, 43, 41, 44, 51, 50] or by some other heuristic scheme [36, 42] The hallmarks of this rst phase are: The main source of data was the patient record. The computational technology was not ecient enough for fuzzy signal or image processing. Most of the fuzzy aid in medicine was linked with AI, and fuzzy sets were ....

K.P. Adlassnig. Fuzzy set theory in medical diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, SMC-16(2):260{ 265, 1986.

....between them: f ( pro , con ) pro a ( 1 con b ) a and b are tuning parameters whose values are obtained during the training session. 5 A Potential Application to CADIAG 2 As a potential application field the inference engine of the expert system for internal medicine CADIAG 2 [7,8] can be considered. This comment sketches the conditions which make the application of the combination scheme reasonable. The fuzzy reasoning model implemented in CADIAG 2 uses two values relating each symptom X i X to the respective disease D j D: frequency of occurrence o and strength of ....

Adlassnig K.-P. Fuzzy set theory in medical diagnosis, IEEE Transactions on Systems, Man, and Cybernetics, SMC-16, 1986, 260-265.

....[Godo et al. 1989] The CADIAG work of Adlassnig and his colleagues is indeed impressive. However it is especially difficult to put medical expert systems into real world use, by comparison for example to applications in manufacturing. Both the cited paper and more recent papers on CADIAG 2 [Adlassnig et al. 1986; Adlassnig et al. 1993] state only that CADIAG 2 systems are undergoing clinical trials. Similarly, the paper on TAIGER does not claim that the system has been deployed and I could not find any further papers on this system. The cited paper on RUM states it is a development environment and the ....

....and Duerre, 1993] According to the journal paper on this system [Parkinson et al. 1994] it uses the CLIPS shell, which is not founded on fuzzy logic. Zadeh gives three examples of expert systemsusing fuzzy logic as their primary formalism for reasoning about uncertainty: CADIAG 2 again [Adlassnig, 1986] , FRIL [Baldwin and Zhou, 1984] and a system for securities trading (with no citation) Recent papers indicate that FRIL is a programming language [Baldwin and Martin, 1992] and that the trading system has only been tested [Tanaka, 1992] Zadeh also cites papers on systems for acupuncture ....

K.-P. Adlassnig. Fuzzy set theory in medical diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, 16(2):260--265, 1986.

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Adlassnig, K.-P.: Fuzzy Set Theory in Medical Diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, SMC-16, No. 2, March/April 1986, pp. 260-265.

....knowledge in that specific area. 1 appeared in Fuzzy Sets and Systems 61 (1994) pp. 37 42 1 Because medical decision making is based on knowledge that has to take uncertainty such as physiological variability into account, judgement of the current state of a patient is often a matter of degree [1]. Consequently, transition from one stage of a disease to the next is hardly ever abrupt but usually smooth. To allow for smooth transitions, a state monitor is based on a fuzzy automaton rather than on a conventional one. Definition 1 (fuzzy automaton) A fuzzy automaton is a quadruple A ....

K.-P. Adlassnig, Fuzzy Set Theory in Medical Diagnosis, IEEE Transactions on Systems, Man, and Cybernetics SMC 16 (1986) 260-265

....actual value is trusted to stem. As opposed to other models of uncertainty employing intervals, in FUZZYBASE the boundaries of a range can be blurred rather than sharp to model a continuous transition from possible to impossible values. Ranges of this kind are ideally modelled through fuzzy sets [1, 2]. To express that an unknown value x is known to be within a given range R represented by a fuzzy set, we write x:R. 2 x no longer stands for values of the universe, but it is restricted to members of R, i.e. holds. R (x) 0 Extending (1) with fuzzily restricted time and value then yields ....

Adlassnig, K.-P., "Fuzzy Set Theory in Medical Diagnosis", IEEE Transactions on Systems, Man, and Cybernetics 16 (1986) 260--265

No context found.

K.P. Adlassnig. Fuzzy set theory in medical diagnosis. IEEE Transactions Systems Man Cybernetics, 16(2):260--265, 1986.

No context found.

K.-P. Adlassnig, "Fuzzy set theory in medical diagnosis," IEEE Tran. Syst., Man, Cybern., vol. 16, pp. 260--265, 1986.

No context found.

Adlassnig K.-P. Fuzzy set theory in medical diagnosis, IEEE Transactions on Systems, Man, and Cybernetics, SMC-16, 1986, 260-265.

No context found.

K.P. Adlassnig, "Fuzzy set theory in medical diagnosis," IEEE Transactions Systems Man Cybernetics, vol. 16, no. 2, pp. 260--265, 1986.

No context found.

K.P. Adlassnig. Fuzzy set theory in medical diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, 16(2):260--265, 1986.

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