Scheme for Unstructured Knowledge Representation in Medical Expert System for Low Back Pain Management

Santra, Debarpita; Sadhukhan, Sounak; Basu, Swapan; Das, Sayantika; Sinha, Sushant K.; Goswami, Subrata

doi:10.1007/978-981-13-1927-3_4

Cited by 3 publications

(1 citation statement)

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“…Information about a patient will be collected through the clinical parameters belonging to set A. The inputted patient information is also structurally represented using frames [33]. The frame that stores patient information is named as "I/P-frame", and three categories of I/P frames are designed in this context: I/P-frame h that captures patient's clinical history through the clinical attributes in A 1 , I/P-frame g that captures patient's local/general examination information using the attributes mentioned in A 2 , I/Pframe i that stores patient's clinical investigation reports through the attributes mentioned in A 3 .…”

Section: Matching Process For Inferencingmentioning

confidence: 99%

Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network

Santra

Mandal

Basu

et al. 2020

Med Biol Eng Comput

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Aiming at developing a medical expert system for low back pain management, the paper proposes an efficient knowledge representation scheme using frame data structures, and also derives a reliable resolution logic through Bayesian Network. When a patient comes to the intended expert system for diagnosis, the proposed inference engine outputs a number of probable diseases in sorted order, with each disease being associated with a numeric measure to indicate its possibility of occurrence. When two or more diseases in the list have the same or closer possibility of occurrence, Bayesian Network is used for conflict resolution. The proposed scheme has been validated with cases of empirically selected thirty patients. Considering the expected value 0.75 as level of acceptance, the proposed system offers the diagnostic inference with the standard deviation of 0.029. The computational value of Chi-Squared test has been obtained as 11.08 with 12 degree of freedom, implying that the derived results from the designed system conform the homogeneity with the expected outcomes. Prior to any clinical investigations on the selected low back pain patients, the accuracy level (average) of 73.89% has been achieved by the proposed system, which is quite close to the expected clinical accuracy level of 75%.

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Section: Matching Process For Inferencingmentioning

confidence: 99%