Differential Diagnostic Reasoning Method for Benign Paroxysmal Positional Vertigo Based on Dynamic Uncertain Causality Graph

Abstract

The accurate differentiation of the subtypes of benign paroxysmal positional vertigo (BPPV) can significantly improve the efficacy of repositioning maneuver in its treatment and thus reduce unnecessary clinical tests and inappropriate medications. In this study, attempts have been made towards developing approaches of causality modeling and diagnostic reasoning about the uncertainties that can arise from medical information. A dynamic uncertain causality graph-based differential diagnosis model for BPPV including 354 variables and 885 causality arcs is constructed. New algorithms are also proposed for differential diagnosis through logical and probabilistic inference, with an emphasis on solving the problems of intricate and confounding disease factors, incomplete clinical observations, and insufficient sample data. This study further uses vertigo cases to test the performance of the proposed method in clinical practice. The results point to high accuracy, a satisfactory discriminatory ability for BPPV, and favorable robustness regarding incomplete medical information. The underlying pathological mechanisms and causality semantics are verified using compact graphical representation and reasoning process, which enhance the interpretability of the diagnosis conclusions.

Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review

“…There is a scope of using embedded systems with trained ML models to help in the early diagnosis of acute vertigo. Attribute weighting and selection methods, Bayesian networks, dynamic, uncertain casualty graphs, decision trees, and random forests can help assess the relative importance of attributes for disease classification [40,43,62]. Lim, E.C.…”

“…Rotational chair testing is employed when seeking evidence of poor VOR suppression and for further quantification of bilateral vestibular loss (BVL). Posturography and gait analyses are now seldom used in the differential diagnosis of vertigo but can help identify vestibular disorders as a cause of imbalance, estimate the risk of falls, monitor the disorder's progression, and track treatment effects [43]. Screening laboratory tests, such as a complete blood count, electrolytes, thyroid function tests, and vitamin B1 and B12 levels and iron studies, are used in seeking the nutritional and metabolic causes of dizziness.…”

“…The other notations used in the Table 2 are as follows: VS-vestibular schwannoma; BPPV-benign paroxysmal positional vertigo; MD-Meniere disease; SDsudden deafness; TV-traumatic vertigo; VNE-vestibular neuritis; VES-vestibulopatia; BRV-benign recurrent vertigo; CL-central lesion; ANE-acoustic neuroma. Machine learning and statistical techniques applied to other questionnaires and multifeature databases containing relevant information about the patients' history, vestibular test findings, symptoms, etc., have also been used for creating intelligent diagnostic systems [21,24,33,37,39,40,43,45,67,68]. Ahmadi, S.A. et al [21] compared machine learning approaches on multi-feature data sets (including a standardized assessment of symptom features, cardiovascular risk factors, and detailed quantitative testing of ocular motor, vestibular, and postural function) vs. clinical scores such as HINTS, ABCD2 for differential diagnosis of vestibular stroke, and vestibular neuritis.…”

“…Dong, C. et al [40] proposes a novel diagnostic and reasoning modeling to identify between 22 etiologies, based on investigations and relevant characteristics of vertigo using DUCG methodology. Dong, C.-L. et al [43] uses a DUCG based differential diagnostic model for subtype differentiation of benign paroxysmal positional vertigo (BPPV). The symptoms, signs, findings of examinations, medical histories, etiologies, and pathogeneses are incorporated in both diagnostic models.…”

Dynamic Uncertain Causality Graph Applied to the Intelligent Evaluation of a Shale-Gas Sweet Spot

“…In recent years, the knowledge representation and reasoning methods based on the dynamic uncertainty causality graph (DUCG) have made great progress in industrial system fault diagnosis [15][16][17] and the auxiliary diagnosis of diseases [18][19][20][21]. In this study, the DUCG was applied to predict a shale-gas sweet spot for the first time.…”