Dynamic Uncertain Causality Graph Applied to Dynamic Fault Diagnoses of Large and Complex Systems

Zhang, Qin; Geng, Shichao

doi:10.1109/tr.2015.2416332

Cited by 46 publications

(15 citation statements)

References 32 publications

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“…Since the existing inference methods are no longer applicable to the hybrid DUCG model, a new inference algorithm for the hybrid DUCG described as follows: 1) Simplification. Simplify the hybrid DUCG by simplification rules 1-10 and 16 [36], [41] based on the received evidence E. Simplification can not only eliminate unrelated variables, causal relationships, and hypotheses but also clearly express the causal relationship between possible hypotheses and evidence. Moreover, it can reduce the complexity of the probability calculation without losing accuracy.…”

Section: Inference Methods Of the Hybrid Ducgmentioning

confidence: 99%

“…As a graphical causal probability model [35]- [40], the DUCG can intuitively express causal knowledge in various graphic symbols, present the result in the form of conditional probability and illustrate it graphically. With initial application in the fault diagnosis of complex large scale systems [41]- [45], the DUCG was later applied to clinical diagnoses, such as jaundice, vertigo, and nasal obstruction, and perfect results were achieved [46], [47]. Due to the good performance of DUCG in clinical diagnosis, this paper uses the DUCG to perform outpatient triage.…”

Section: Introductionmentioning

confidence: 99%

“…In the DUCG, different types of variables/events are represented by different shapes [41]. As shown in Figure 1, B i ( ) is the root cause event, such as a fault source in the fault diagnosis or a disease in the clinical diagnosis.…”

Section: Brief Introduction To the Ducgmentioning

confidence: 99%

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A General Outpatient Triage System Based on Dynamic Uncertain Causality Graph

Lü

Zhang

et al. 2020

IEEE Access

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In order to reduce the misdiagnosis caused by outpatient triage error and help triage nurses to improve their triage accuracy, a general outpatient triage system based on the hybrid Dynamic Uncertain Causality Graph (hybrid DUCG) is presented. The hybrid DUCG is a combination of the single-valued DUCG (S-DUCG) and the multivalued DUCG (M-DUCG). In this paper, the M-DUCG is utilized to construct the basic outpatient triage knowledge base. In the knowledge base, each department is modeled as one sub-M-DUCG. It describes the causal relations between the department and its associated clinical features. All the sub-M-DUCGs are compiled into one M-DUCG as the basic outpatient triage knowledge base. The S-DUCG is employed to adjust the basic knowledge base to match the variant triage requirements for various hospitals. To validate the triage system, we constructed a basic triage knowledge base and adjusted it to match the triage requirement of a county-level hospital. Verification experiments were performed based on the adjusted triage knowledge base. The result showed that the triage system has high triage accuracy, and it can effectively assist triage nurses in outpatient triage.

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Section: Inference Methods Of the Hybrid Ducgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A General Outpatient Triage System Based on Dynamic Uncertain Causality Graph

Lü

Zhang

et al. 2020

IEEE Access

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“…To overcome these defects, a new model named as Dynamic Uncertain Causality Graph (DUCG) is presented [23][24][25]. DUCG has been successfully applied in the fault diagnoses of industrial systems [26] and medical diagnoses of vertigo [27].…”

Section: Introductionmentioning

confidence: 99%

Towards Dynamic Uncertain Causality Graphs for the Intelligent Diagnosis and Treatment of Hepatitis B

Deng

Zhang

2020

Symmetry

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Hepatitis B is a widespread epidemic in the world, but so far no single drug has been shown to kill or eliminate the Hepatitis B virus and heal people with chronic Hepatitis B virus infection. Based on comprehensive investigations to relevant characteristics of Hepatitis B, a diagnostic modelling and reasoning methodology using Dynamic Uncertain Causality Graph is proposed. The symptoms, physical signs, examinations results, medical histories, etiology, pathogenesis and other factors were included in the diagnosis model. In order to reduce the difficulty of building the model, a modular modeling scheme is proposed, which provides multi-perspectives and arbitrary granularity for the expression of disease causality. The chain reasoning algorithm and weighted logic operation mechanism are introduced to ensure the correctness and effectiveness of diagnostic reasoning under incomplete and uncertain information. In addition, the causal view of the potential interactions between diseases and symptoms visually shows the reasoning process in a graphical way. In the relevant model, the model of the diagnostic process and the model of the therapeutic process are symmetrical. The results show that, even with incomplete observations, the proposed methodology achieves encouraging diagnostic accuracy and effectiveness, providing a promising assistance tool for physicians in the diagnosis of Hepatitis B.

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“…DUCG is a probabilistic graphical model which intuitively expresses a causal relationship among variables in an explicit pattern, and uses a "chaining" inference algorithm to achieve efficient reasoning. DUCG can propagate probabilities through causality chains, achieve dynamic reasoning either with or without spread of causality between time slices (Zhang and Geng, 2015), achieve reasoning in the case of logic circles (Zhang, 2015a), and handle fuzzy evidence (Zhang, 2015b). The greatest advantage of DUCG in clinical diagnosis is that it can display the reasoning process and results graphically, and make an inference with incomplete information and less accurate parameters than conventional methods such as Bayesian Networks.…”

Section: Introductionmentioning

confidence: 99%

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model

Hao

Geng

Fan

et al. 2017

J. Zhejiang Univ. Sci. B

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Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

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Dynamic Uncertain Causality Graph Applied to Dynamic Fault Diagnoses of Large and Complex Systems

Cited by 46 publications

References 32 publications

A General Outpatient Triage System Based on Dynamic Uncertain Causality Graph

A General Outpatient Triage System Based on Dynamic Uncertain Causality Graph

Towards Dynamic Uncertain Causality Graphs for the Intelligent Diagnosis and Treatment of Hepatitis B

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model

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