Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

Jiao, Yang; Zhang, Zhan; Zhang, Ting; Shi, Wen; Zhu, Yan; Hu, Jie; Zhang, Qin

doi:10.1007/s11684-020-0762-0

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…(3) Collaborate with data-driven medical AI, so that the useful information included in medical images and sounds can be extracted as the input of DUCG; (4) Develop more DUCG models for rare disease diagnoses as shown in Jiao et al (2020)…”

Section: Key Idea and Future Workmentioning

confidence: 99%

“…To overcome the above problems and provide a trustworthy medical AI for clinical diagnosis, DUCG was developed (Zhang et al 2021;Zhang 2012, Nie and Zhang 2021, Zhang 2015a, b, Hao et al 2017, Zhang and Yao 2018, Dong and Zhang 2020, Qiu and Zhang 2021, Jiao et al 2020, Ning et al 2020, Deng and Zhang 2020, Zhang and Jiao 2022, Bu et al 2023a, verified by third-party hospitals and applied in real-world.…”

Section: Introductionmentioning

confidence: 99%

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Methodology and real-world applications of dynamic uncertain causality graph for clinical diagnosis with explainability and invariance

Zhang,

Jiao

et al. 2024

Artif Intell Rev

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AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, and invariant across different application scenarios, without problems of data collection, labeling, fitting, privacy, bias, generalization, high cost and high energy consumption. Through close collaboration between clinical experts and DUCG technicians, 46 DUCG models covering 54 chief complaints were constructed. Over 1,000 diseases can be diagnosed without triage. Before being applied in real-world, the 46 DUCG models were retrospectively verified by third-party hospitals. The verified diagnostic precisions were no less than 95%, in which the diagnostic precision for every disease including uncommon ones was no less than 80%. After verifications, the 46 DUCG models were applied in the real-world in China. Over one million real diagnosis cases have been performed, with only 17 incorrect diagnoses identified. Due to DUCG’s transparency, the mistakes causing the incorrect diagnoses were found and corrected. The diagnostic abilities of the clinicians who applied DUCG frequently were improved significantly. Following the introduction to the earlier presented DUCG methodology, the recommendation algorithm for potential medical checks is presented and the key idea of DUCG is extracted.

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Section: Key Idea and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methodology and real-world applications of dynamic uncertain causality graph for clinical diagnosis with explainability and invariance

Zhang,

Jiao

et al. 2024

Artif Intell Rev

Self Cite

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“…DUCG developed in recent years is such a model (Zhang 2012 , 2015a , b ; Zhang et al 2014 , 2018 ; Zhang and Geng 2015 ; Zhang and Zhang 2016 ; Zhang and Yao 2018 ) and has achieved promising application results for fault diagnoses of large, complex industrial systems (Zhang and Yao 2018 ; Zhang et al 2018 ; Dong et al 2014a , 2018 ; Qu et al 2015 ; Zhao et al 2014 ; Geng and Zhang 2014 ) and general clinical diagnoses (Dong et al 2014b ; Hao et al 2017 ; Fan et al 2018 ; Jiao et al 2020 ; Ning et al 2020 ; Zhang et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

AI-aided general clinical diagnoses verified by third-parties with dynamic uncertain causality graph extended to also include classification

et al. 2022

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Artificial intelligence (AI)-aided general clinical diagnosis is helpful to primary clinicians. Machine learning approaches have problems of generalization, interpretability, etc. Dynamic Uncertain Causality Graph (DUCG) based on uncertain casual knowledge provided by clinical experts does not have these problems. This paper extends DUCG to include the representation and inference algorithm for non-causal classification relationships. As a part of general clinical diagnoses, six knowledge bases corresponding to six chief complaints (arthralgia, dyspnea, cough and expectoration, epistaxis, fever with rash and abdominal pain) were constructed through constructing subgraphs relevant to a chief complaint separately and synthesizing them together as the knowledge base of the chief complaint. A subgraph represents variables and causalities related to a single disease that may cause the chief complaint, regardless of which hospital department the disease belongs to. Verified by two groups of third-party hospitals independently, total diagnostic precisions of the six knowledge bases ranged in 96.5–100%, in which the precision for every disease was no less than 80%.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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Shale-gas sweet-spot evaluation as a critical part of shale-gas exploration and development has always been the focus of experts and scholars in the unconventional oil and gas field. After comprehensively considering geological, engineering, and economic factors affecting the evaluation of shale-gas sweet spots, a dynamic uncertainty causality graph (DUCG) is applied for the first time to shale-gas sweet-spot evaluation. A graphical modeling scheme is presented to reduce the difficulty in model construction. The evaluation model is based on expert knowledge and does not depend on data. Through rigorous and efficient reasoning, it guarantees exact and efficient diagnostic reasoning in the case of incomplete information. Multiple conditional events and weighted graphs are proposed for specific problems in shale-gas sweet-spot evaluation, which is an extension of the DUCG that defines only one conditional event for different weighted function events and relies only on the experience of a single expert. These solutions make the reasoning process and results more objective, credible, and interpretable. The model is verified with both complete data and incomplete data. The results show that compared with other methods, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising auxiliary tool for shale-gas sweet spot evaluation.

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Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

Cited by 7 publications

References 13 publications

Methodology and real-world applications of dynamic uncertain causality graph for clinical diagnosis with explainability and invariance

Methodology and real-world applications of dynamic uncertain causality graph for clinical diagnosis with explainability and invariance

AI-aided general clinical diagnoses verified by third-parties with dynamic uncertain causality graph extended to also include classification

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

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