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

Abstract

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%.

Machine Learning in Pain Medicine: An Up-To-Date Systematic Review

“…Most of these algorithms were supervised classification or regression techniques to address issues related to pain. The selected studies used mostly classical ML algorithms, such as SVM [ 9 , 12 , 15 , 21 , 22 , 25 – 27 ], and random forest (RF) [ 14 , 15 , 28 , 29 , 31 , 32 ] followed by Bayes [ 17 , 19 , 20 , 22 ] techniques to classify tasks by assigning a predefined class label to an observation.…”

“…Bayes [17,19,20,22] Estimates the probability of data patterns belonging to a specific class Boosting: functional data boosting (FDboost) [13]; gradient boosting (GB) [24,28]; extreme gradient boosting regression (XGBoost) [27,31] Merges weak classifiers into strong ones Deep learning neural network (DLNN) [10,11,14,16,18,34,35] Similarly to multiple linear regression it contains layers of interconnected nodes. A subclass of NN is the convolutional neural network (CNN)…”

“…Grauhan et al used CNN for the effective diagnosis of the cause of shoulder pain on 2700 plain radiographs (X-rays) with the best accuracy to be for osteoarthritis (87%), even when presented with poor image quality [30]. Santra [17]. Prediction of manifestation of pain was also one of the commonest reasons for performing ML analysis.…”

Transformer encoder with multiscale deep learning for pain classification using physiological signals

“…Bayesian models have also found their place in this domain, with notable work by Santra et al . ( Santra et al, 2020 ). Tree-based models, especially XGBoost and AdaBoost, have been frequently utilized, with research by Shi et al .…”

“…Models such as k-Nearest Neighbors (KNN) were explored by Cao et al (Cao et al, 2021), while the Support Vector Machine (SVM) approach was researched by Campbell et al (Campbell et al, 2019). Bayesian models have also found their place in this domain, with notable work by Santra et al (Santra et al, 2020). Tree-based models, especially XGBoost and AdaBoost, have been frequently utilized, with research by Shi et al (Shi et al, 2022), Naeini et al (Naeini et al, 2021), and Cao et al (Cao et al, 2021) leading the way.…”