The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo

• Published in: Computer methods and programs in biomedicine Vol. 113; no. 1; pp. 162 - 174
• Main Authors: Dong, Chunling, Wang, Yanjun, Zhang, Qin, Wang, Ningyu
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.01.2014; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Causality; Clinical decision-making; Computer science; control theory; systems; Computerized, statistical medical data processing and models in biomedicine; Diagnosis; Diagnostic systems; Disease diagnosis; Disorders of higher nervous function. Focal brain diseases. Central vestibular syndrome and deafness. Brain stem syndromes; Dynamic Uncertain Causality Graph; Dynamics; Exact sciences and technology; Graphical knowledge representation; Graphs; Humans; Inference from stochastic processes; time series analysis; Internal Medicine; Mathematical models; Mathematics; Medical management aid. Diagnosis aid; Medical sciences; Methodology; Models, Biological; Nervous system (semeiology, syndromes); Neurology; Other; Probabilistic inference; Probability and statistics; Reasoning; Sciences and techniques of general use; Statistics; Uncertainty; Vertigo; Vertigo - diagnosis; Dynamic Uncertain Causality Graph; Disease diagnosis; Clinical decision-making; Probabilistic inference; Vertigo; Graphical knowledge representation; Knowledge engineering; Disease; Equity; History; Modeling; Uncertain system; Efficiency; Diagnosis; Dynamic programming; Incomplete information; Medical application; Grain size; Diagnostic reasoning; Empirical method; Inference; Graph theory; Experimental study; Weight; Medicine; Graphics; Neurology; Imperfect information; Causality
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2013.10.002

Abstract Vertigo is a common complaint with many potential causes involving otology, neurology and general medicine, and it is fairly difficult to distinguish the vertiginous disorders from each other accurately even for experienced physicians. Based on comprehensive investigations to relevant characteristics of vertigo, we propose a diagnostic modeling and reasoning methodology using Dynamic Uncertain Causality Graph. The symptoms, signs, findings of examinations, medical histories, etiology and pathogenesis, and so on, are incorporated in the diagnostic model. A modularized modeling scheme is presented to reduce the difficulty in model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. We resort to the “chaining” inference algorithm and weighted logic operation mechanism, which guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal insights into underlying interactions among diseases and symptoms intuitively demonstrate the reasoning process in a graphical manner. These solutions make the conclusions and advices more explicable and convincing, further increasing the objectivity of clinical decision-making. Verification experiments and empirical evaluations are performed with clinical vertigo cases. The results reveal that, even with incomplete observations, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising assistance tool for physicians in diagnosis of vertigo.