{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T15:06:21Z","timestamp":1777820781368,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Institute for Guo Qiang, Tsinghua University","award":["2020QG0001"],"award-info":[{"award-number":["2020QG0001"]}]},{"DOI":"10.13039\/501100002865","name":"Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["cstc2018jscx-mszdx0106"],"award-info":[{"award-number":["cstc2018jscx-mszdx0106"]}],"id":[{"id":"10.13039\/501100002865","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Rockefeller-Endowed China Medical Board","award":["20-384"],"award-info":[{"award-number":["20-384"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2013100%, in which the precision for every disease was no less than 80%.<\/jats:p>","DOI":"10.1007\/s10462-021-10109-w","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T16:02:31Z","timestamp":1643472151000},"page":"4485-4521","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["AI-aided general clinical diagnoses verified by third-parties with dynamic uncertain causality graph extended to also include classification"],"prefix":"10.1007","volume":"55","author":[{"given":"Zhan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yang","family":"Jiao","sequence":"additional","affiliation":[]},{"given":"Mingxia","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Juan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Fengwei","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1214-9657","authenticated-orcid":false,"given":"Qin","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"key":"10109_CR1","doi-asserted-by":"crossref","unstructured":"Danal Bardou, Kun Zhang, Sayed Mohammad Ahmad. Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access, vol. 6, pp. 24680\u201324693. 2018.","DOI":"10.1109\/ACCESS.2018.2831280"},{"issue":"5","key":"10109_CR2","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1109\/TMI.2016.2528821","volume":"35","author":"T Brosch","year":"2016","unstructured":"Brosch T et al (2016) Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans Medical Imaging 35(5):1229\u20131239","journal-title":"IEEE Trans Medical Imaging"},{"key":"10109_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2995087","author":"X Bu","year":"2020","unstructured":"Bu X, Lu L, Zhang Z, Zhang Q, Yan Z (2020) A general outpatient triage system based on dynamic uncertain causality graph. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2020.2995087","journal-title":"IEEE Access"},{"issue":"3","key":"10109_CR4","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.1109\/JBHI.2013.2289367","volume":"18","author":"S Ceccon","year":"2014","unstructured":"Ceccon S, Garwayheath DF, Crabb DP et al (2014) Exploring early Glaucoma and the visual field test: classification and clustering using bayesian networks. IEEE J Biomed Health Infom 18(3):1008\u20131014","journal-title":"IEEE J Biomed Health Infom"},{"issue":"1","key":"10109_CR5","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/JBHI.2016.2636929","volume":"21","author":"S Christodoulidis","year":"2017","unstructured":"Christodoulidis S, Anthimopoulos M, Ebner L, Chresti A, Mougiakakou S (2017) Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE J Biomed Health Inform 21(1):76\u201384","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"10109_CR6","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1007\/s11633-014-0791-8","volume":"11","author":"C Dong","year":"2014","unstructured":"Dong C, Zhang Q, Geng S (2014a) A modeling and probabilistic reasoning method of dynamic uncertain causality graph for industrial fault diagnosis. Int J Autom Comput 11(3):288\u2013298","journal-title":"Int J Autom Comput"},{"key":"10109_CR7","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.cmpb.2013.10.002","volume":"113","author":"C Dong","year":"2014","unstructured":"Dong C, Wang Y, Zhang Q, Wang N (2014b) The methodology of dynamic uncertain causality graph for intelligent diagnosis of vertigo. Comput Methods Programs Biomed 113:62\u2013174","journal-title":"Comput Methods Programs Biomed"},{"issue":"7","key":"10109_CR8","first-page":"614","volume":"58","author":"C Dong","year":"2018","unstructured":"Dong C, Zhao Y, Zhang Q (2018) Cubic causality modeling and uncertain inference method for dynamic fault diagnosis. J Tsinghua Univ (Sci Technol) 58(7):614\u2013622","journal-title":"J Tsinghua Univ (Sci Technol)"},{"issue":"8","key":"10109_CR9","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1049\/iet-cvi.2016.0425","volume":"11","author":"Sawaswathi Duraisamy","year":"2017","unstructured":"Duraisamy Sawaswathi, Emperumal Srinivasan (2017) Computer-aided mammogram diagnosis system using deep learing convolutional fully complex-valued relaxation neural network classifier. IET Computer Vision 11(8):656\u2013662","journal-title":"IET Computer Vision"},{"key":"10109_CR10","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1166\/jmihi.2016.1606","volume":"1","author":"O Er","year":"2016","unstructured":"Er O, Cetin O, Bascil MS, Temurtas F (2016) A comparitive study on Parkinson\u2019s disease diagnosis using neural networks and artifial immune system. J Med Imaging Health Inf 1:264\u2013268","journal-title":"J Med Imaging Health Inf"},{"key":"10109_CR11","first-page":"249","volume":"06","author":"Y Fan","year":"2018","unstructured":"Fan Y, Zhang Z, Jing Z, Wang Y, Liu Z, Guo M, Wang R, Feng M (2018) Diagnostic value of dynamic uncertain causality graph DUCG in sellar region disease. Chinese J Minimal Invasive Neurosurg 06:249\u2013253","journal-title":"Chinese J Minimal Invasive Neurosurg"},{"key":"10109_CR12","volume-title":"Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition","author":"K Fukushima","year":"1982","unstructured":"Fukushima K, Miyake S (1982) Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. Competition and Cooperation in Neural Nets. Springer, Berlin Heidelberg"},{"key":"10109_CR13","unstructured":"Geng S and Zhang Q (2014) Calculation method to diagnose intigrated causes of faults in process systems by means of dynamic uncertain causality graph. In: proceeding of 2014 Aisa-Pasific computer science and application confreence (CSAC 2014), Shanghai, China, pp 306\u2013311"},{"issue":"5","key":"10109_CR14","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1631\/jzus.B1600273","volume":"18","author":"S Hao","year":"2017","unstructured":"Hao S, Geng S, Fan L, Chen J, Zhang Q, Li L (2017) Intelligent diagnosis of jaundice with dynamic uncertain causality graph model. J Zhejiang Univ-Sci B (Biomed Biotechnol) 18(5):393\u2013401","journal-title":"J Zhejiang Univ-Sci B (Biomed Biotechnol)"},{"key":"10109_CR15","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1007\/s11684-020-0762-0","volume":"14","author":"Y Jiao","year":"2020","unstructured":"Jiao Y, Zhang Z, Zhang T, Shi W, Zhu Y, Hu J, Zhang Q (2020) Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea. Front Med 14:488\u2013497","journal-title":"Front Med"},{"key":"10109_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0335-9","author":"H Liang","year":"2019","unstructured":"Liang H, Tsui BY, Ni H, Calentim CCS, Baxter SL, Liu G et al (2019) Evaluation and accurate diagnoses of pdiatric diseases using artificial intelligence. Nat Med. https:\/\/doi.org\/10.1038\/s41591-018-0335-9","journal-title":"Nat Med"},{"issue":"7","key":"10109_CR17","doi-asserted-by":"publisher","first-page":"1854","DOI":"10.1109\/TIFS.2018.2806741","volume":"13","author":"Z Lin","year":"2018","unstructured":"Lin Z, Huang Y, Wang J (2018) RNN-SM fast steganalysis of VoIP streams using recurrent neural network. IEEE Trans Inf Forensics Secur 13(7):1854\u20131868","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"4","key":"10109_CR18","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/42.476112","volume":"14","author":"SB Lo","year":"1995","unstructured":"Lo SB, Lou SA, Lin JS et al (1995) Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans Med Imaging 14(4):711","journal-title":"IEEE Trans Med Imaging"},{"key":"10109_CR19","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1007\/s11684-020-0791-8","volume":"14","author":"D Ning","year":"2020","unstructured":"Ning D, Zhang Z, Qiu K, Lu L, Zhang Q, Zhu Y, Wang R (2020) Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of sex development. Frontiers of Medicine 14:498\u2013505","journal-title":"Frontiers of Medicine"},{"issue":"3","key":"10109_CR20","first-page":"354","volume":"10","author":"Y Qu","year":"2015","unstructured":"Qu Y, Zhang Q, Zhu X (2015) Application of dynamic uncertain causality graph to dynamic fault diagnosis in chemical processes. CAAI Trans Intell Syst 10(3):354\u2013361","journal-title":"CAAI Trans Intell Syst"},{"issue":"3","key":"10109_CR21","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211\u2013252","journal-title":"Int J Comput Vision"},{"issue":"5","key":"10109_CR22","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"H-C Shin","year":"2016","unstructured":"Shin H-C et al (2016) Deep comvolutional neural networks for computer-aided detection: CNN architectures, dataset charicteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285\u20131294","journal-title":"IEEE Trans Med Imaging"},{"key":"10109_CR23","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the Inception Architecture for Computer Vision. Computer Sci, 2015: 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"10109_CR24","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-06799-6","author":"J Wu","year":"2018","unstructured":"Wu J, Liu X, Zhang X, He Z, Lv P (2018) Mastrer clinical medical knowledge at certified-doctor-level with deep learning model. Nacture Commun. https:\/\/doi.org\/10.1038\/s41467-018-06799-6","journal-title":"Nacture Commun"},{"issue":"1","key":"10109_CR25","first-page":"113","volume":"3","author":"Q Yao","year":"2017","unstructured":"Yao Q, Zhang Q, Liu P, Yang P (2017) Application of dynamic uncertain causality graph in spacecraft fault diagnosis: prediction. Int Core J Eng 3(1):113\u2013119","journal-title":"Int Core J Eng"},{"issue":"1","key":"10109_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11390-012-1202-7","volume":"27","author":"Q Zhang","year":"2012","unstructured":"Zhang Q (2012) Dynamic uncertain causality graph for knowledge representation and reasoning: discrete DAG cases J. Comput Sci Technol 27(1):1\u201323","journal-title":"Comput Sci Technol"},{"issue":"7","key":"10109_CR27","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1109\/TNNLS.2015.2402162","volume":"26","author":"Q Zhang","year":"2015","unstructured":"Zhang Q (2015) Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: directed cyclic graph and joint probability distribution. IEEE Trans Neural Netw Learn Syste 26(7):1503\u20131517","journal-title":"IEEE Trans Neural Netw Learn Syste"},{"issue":"1","key":"10109_CR28","first-page":"59","volume":"11","author":"Q Zhang","year":"2018","unstructured":"Zhang Q (2018) Increase safety and availability of nuclear power plants by means of DUCG. China Nuclear Power 11(1):59\u201368","journal-title":"China Nuclear Power"},{"issue":"3","key":"10109_CR29","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1109\/TR.2015.2416332","volume":"64","author":"Q Zhang","year":"2015","unstructured":"Zhang Q, Geng S (2015) Dynamic uncertain causality graph applied to dynamic fault diagnosis of large and complex systems. IEEE Trans Rel 64(3):910\u2013927","journal-title":"IEEE Trans Rel"},{"issue":"5","key":"10109_CR30","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1109\/TNNLS.2017.2673243","volume":"29","author":"Q Zhang","year":"2018","unstructured":"Zhang Q, Yao Q (2018) Dynamic uncertain causality graph for knowledge representation and reasoning: utilization of statistical data and domain knowledge in complex cases. IEEE Trans Neural Netw Learn Syst 29(5):1637\u20131651","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"10109_CR31","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1109\/TR.2015.2503759","volume":"65","author":"Q Zhang","year":"2016","unstructured":"Zhang Q, Zhang Z (2016) Dynamic uncertain causality graph applied to dynamic fault diagnoses and predictions with negative feedbacks. IEEE Trans Rel 65(2):1030\u20131044","journal-title":"IEEE Trans Rel"},{"issue":"4","key":"10109_CR32","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TNNLS.2013.2279320","volume":"25","author":"Q Zhang","year":"2014","unstructured":"Zhang Q, Dong C, Cui Y, Yang Z (2014) Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: statistics base, matrix and fault diagnosis. IEEE Trans Neural Netw Learn Syst 25(4):645\u2013663","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"7","key":"10109_CR33","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/CC.2018.8424610","volume":"15","author":"Q Zhang","year":"2018","unstructured":"Zhang Q, Qiu K, Zhang Z (2018) Calculate joint probability distribution of steady directed cyclic graph with local data and domain casual knowledge. China Comun 15(7):146\u2013155","journal-title":"China Comun"},{"key":"10109_CR34","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s10462-020-09871-0","volume":"54","author":"Q Zhang","year":"2021","unstructured":"Zhang Q, Bu X, Zhang Z, Zhang M, Hu J (2021) Dynamic uncertain causality graph for computer-aided general clinical diagnoses with nasal obstruction as illustration. Artif Intell Rev 54:27\u201361","journal-title":"Artif Intell Rev"},{"key":"10109_CR35","doi-asserted-by":"crossref","unstructured":"Zhang Q (2015) Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: continuous variable, uncertain evidence and failure forecast. IEEE Trans Syst, Man Cybern,. 45, 7, pp 990\u20131003","DOI":"10.1109\/TSMC.2015.2392711"},{"key":"10109_CR36","first-page":"496","volume":"48","author":"Y Zhao","year":"2014","unstructured":"Zhao Y, Zhang Q, Dong C (2014) Application of DUCG in fault diagnosis of nuclear power plant secondary loop. Autom Sci Technol 48:496\u2013501","journal-title":"Autom Sci Technol"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-021-10109-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-021-10109-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-021-10109-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T17:52:47Z","timestamp":1657734767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-021-10109-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,29]]},"references-count":36,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10109"],"URL":"https:\/\/doi.org\/10.1007\/s10462-021-10109-w","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,29]]},"assertion":[{"value":"22 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}