{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:23:58Z","timestamp":1773480238364,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:00:00Z","timestamp":1740182400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:00:00Z","timestamp":1740182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Scientific Research Project of Anhui Provincial Health Commission","award":["AHWJ2022b058"],"award-info":[{"award-number":["AHWJ2022b058"]}]},{"name":"Joint Fund for Medical Artificial Intelligence of the First Affiliated Hospital of USTC","award":["MAI2022Q009"],"award-info":[{"award-number":["MAI2022Q009"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Memetic Comp."],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s12293-025-00439-5","type":"journal-article","created":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T07:38:57Z","timestamp":1740209937000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Clinical causal analysis via iterative active structure learning"],"prefix":"10.1007","volume":"17","author":[{"given":"Zhenchao","family":"Tao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiyan","family":"Chi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lyuzhou","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taiyu","family":"Ban","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Tu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,22]]},"reference":[{"key":"439_CR1","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/s12293-020-00312-7","volume":"12","author":"N Noman","year":"2020","unstructured":"Noman N, Moscato P (2020) Designing optimal combination therapy for personalised glioma treatment. Memetic Comput 12:317\u2013329","journal-title":"Memetic Comput"},{"key":"439_CR2","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s12293-014-0135-9","volume":"6","author":"G Schaefer","year":"2014","unstructured":"Schaefer G (2014) Aco classification of thermogram symmetry features for breast cancer diagnosis. Memetic Comput 6:207\u2013212","journal-title":"Memetic Comput"},{"key":"439_CR3","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s12293-010-0043-6","volume":"2","author":"M Cacciola","year":"2010","unstructured":"Cacciola M, Megali G, Fiasch\u00e9 M, Versaci M, Morabito FC (2010) A comparison between neural networks and k-nearest neighbours for blood cells taxonomy. Memetic Comput 2:237\u2013246","journal-title":"Memetic Comput"},{"issue":"12","key":"439_CR4","doi-asserted-by":"publisher","first-page":"5883","DOI":"10.1109\/JBHI.2022.3212863","volume":"26","author":"X Wang","year":"2022","unstructured":"Wang X, Li Y, Ban T, Zhu J, Chen L, Usman M, Wang X, Chen H, Chen X, Leung C et al (2022) Dynamic link prediction for discovery of new impactful COVID-19 research approaches. IEEE J Biomed Health Inform 26(12):5883\u20135894","journal-title":"IEEE J Biomed Health Inform"},{"key":"439_CR5","doi-asserted-by":"crossref","unstructured":"Wang X, Chen L, Lyu D, Ban T, Guan Y, Chen Q (2022) Research concept link prediction via graph convolutional network. In: 2022 8th International Conference on Big Data and Information Analytics (BigDIA), pages 220\u2013225. IEEE","DOI":"10.1109\/BigDIA56350.2022.9874237"},{"issue":"1","key":"439_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-019-1004-8","volume":"19","author":"S Uddin","year":"2019","unstructured":"Uddin S, Khan A, Hossain ME, Moni MA (2019) Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 19(1):1\u201316","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"439_CR7","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/s12886-023-02898-1","volume":"23","author":"Y Zhi","year":"2023","unstructured":"Zhi Y, Cai M, Rui D, Qiao Y, Zheng X, Guanghua X, Yan L, Dianpeng W (2023) Quantitative evaluation of anisometropic amblyopia treatment efficacy by coupling multiple visual functions via critic algorithm. BMC Ophthalmol 23(1):162","journal-title":"BMC Ophthalmol"},{"key":"439_CR8","doi-asserted-by":"crossref","unstructured":"Yin Q, Zhong L, Song Y, Bai L, Wang Z, Li C, Xu Y, Yang X (2023) A decision support system in precision medicine: contrastive multimodal learning for patient stratification. Annals of Operations Research, pages 1\u201329","DOI":"10.1007\/s10479-023-05545-6"},{"key":"439_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3335484","volume":"61","author":"X Wang","year":"2023","unstructured":"Wang X, Chen L, Ban T, Lyu D, Guan Y, Xingyu W, Zhou X, Chen H (2023) Accurate label refinement from multiannotator of remote sensing data. IEEE Trans Geosci Remote Sens 61:1\u201313","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"439_CR10","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.fmre.2021.09.003","volume":"1","author":"X Wang","year":"2021","unstructured":"Wang X, Chen L, Ban T, Usman M, Guan Y, Liu S, Tianhao W, Chen H (2021) Knowledge graph quality control: a survey. Fundamental Res 1(5):607\u2013626","journal-title":"Fundamental Res"},{"key":"439_CR11","doi-asserted-by":"crossref","unstructured":"Xu F, Uszkoreit H, Du Y, Fan W, Zhao D, Zhu J (2019) Explainable ai: A brief survey on history, research areas, approaches and challenges. In Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9\u201314, 2019, Proceedings, Part II 8, pages 563\u2013574. Springer","DOI":"10.1007\/978-3-030-32236-6_51"},{"key":"439_CR12","unstructured":"Chattopadhyay A, Manupriya P, Sarkar A, Balasubramanian VN (2019) Neural network attributions: A causal perspective. In International Conference on Machine Learning, pages 981\u2013990. PMLR"},{"issue":"8","key":"439_CR13","doi-asserted-by":"publisher","first-page":"8721","DOI":"10.1007\/s10462-022-10351-w","volume":"56","author":"NK Kitson","year":"2023","unstructured":"Kitson NK, Constantinou AC, Guo Z, Liu Y, Chobtham K (2023) A survey of Bayesian network structure learning. Artif Intell Rev 56(8):8721\u20138814","journal-title":"Artif Intell Rev"},{"key":"439_CR14","unstructured":"Mani S, Cooper GF (2000) Causal discovery from medical textual data. In Proceedings of the AMIA Symposium, page 542. American Medical Informatics Association"},{"issue":"12","key":"439_CR15","doi-asserted-by":"publisher","first-page":"e82349","DOI":"10.1371\/journal.pone.0082349","volume":"8","author":"MB Sesen","year":"2013","unstructured":"Sesen MB, Nicholson AE, Banares-Alcantara R, Kadir T, Brady M (2013) Bayesian networks for clinical decision support in lung cancer care. PLoS ONE 8(12):e82349","journal-title":"PLoS ONE"},{"key":"439_CR16","unstructured":"Chen L, Wang X, Ban T, Usman M, Liu S, Lyu D, Chen H (2022) Research ideas discovery via hierarchical negative correlation. IEEE Transactions on Neural Networks and Learning Systems"},{"key":"439_CR17","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s41060-016-0032-z","volume":"3","author":"J Ramsey","year":"2017","unstructured":"Ramsey J, Glymour M, Sanchez-Romero R, Glymour C (2017) A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images. Int J Data Sci Anal 3:121\u2013129","journal-title":"Int J Data Sci Anal"},{"key":"439_CR18","doi-asserted-by":"crossref","unstructured":"Jaber A, Zhang J, Bareinboim E (2019) Causal identification under markov equivalence: Completeness results. In International Conference on Machine Learning, pages 2981\u20132989. PMLR","DOI":"10.24963\/ijcai.2019\/859"},{"key":"439_CR19","doi-asserted-by":"crossref","unstructured":"Ni Y L, Zhang K, Yuan C (2021) Improving causal discovery by optimal Bayesian network learning. In Proceedings of the AAAI Conference on Artificial Intelligence 35:8741\u20138748","DOI":"10.1609\/aaai.v35i10.17059"},{"issue":"271","key":"439_CR20","first-page":"1","volume":"24","author":"YS Wang","year":"2023","unstructured":"Wang YS, Drton M (2023) Causal discovery with unobserved confounding and non-gaussian data. J Mach Learn Res 24(271):1\u201361","journal-title":"J Mach Learn Res"},{"issue":"5","key":"439_CR21","first-page":"152","volume":"22","author":"JC Westland","year":"2015","unstructured":"Westland JC (2015) Structural equation models. Stud Syst Decis Control 22(5):152","journal-title":"Stud Syst Decis Control"},{"key":"439_CR22","doi-asserted-by":"crossref","unstructured":"Ben-Gal I (2008) Bayesian networks. Encyclopedia of statistics in quality and reliability","DOI":"10.1002\/9780470061572.eqr089"},{"key":"439_CR23","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"J Pearl","year":"2009","unstructured":"Pearl J (2009) Causality. Cambridge university press, Cambridge"},{"issue":"4","key":"439_CR24","first-page":"4964","volume":"45","author":"W Xingyu","year":"2022","unstructured":"Xingyu W, Jiang B, Zhong Y, Chen H (2022) Multi-target markov boundary discovery: Theory, algorithm, and application. IEEE Trans Pattern Anal Mach Intell 45(4):4964\u20134980","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"439_CR25","doi-asserted-by":"crossref","unstructured":"Wu X, Jiang B, Wang X, Ban T, Chen H (2023) Feature selection in the data stream based on incremental markov boundary learning. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2023.3249767"},{"key":"439_CR26","doi-asserted-by":"crossref","unstructured":"Waldmann MR, Martignon L (2022) A bayesian network model of causal learning. In Proceedings of the twentieth annual conference of the Cognitive Science Society, pages 1102\u20131107. Routledge","DOI":"10.4324\/9781315782416-198"},{"key":"439_CR27","unstructured":"Entner D, Hoyer PO (2010) On causal discovery from time series data using fci. Probabilistic graphical models, pages 121\u2013128"},{"key":"439_CR28","unstructured":"Li A, Beek P (2018) Bayesian network structure learning with side constraints. In International conference on probabilistic graphical models, pages 225\u2013236. PMLR"},{"key":"439_CR29","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s10994-006-6889-7","volume":"65","author":"I Tsamardinos","year":"2006","unstructured":"Tsamardinos I, Brown LE, Aliferis CF (2006) The max-min hill-climbing Bayesian network structure learning algorithm. Mach Learn 65:31\u201378","journal-title":"Mach Learn"},{"issue":"2","key":"439_CR30","doi-asserted-by":"publisher","first-page":"80","DOI":"10.26599\/IJCS.2022.9100013","volume":"6","author":"J Zhu","year":"2022","unstructured":"Zhu J, Xingyu W, Usman M, Wang X, Chen H (2022) Link prediction in continuous-time dynamic heterogeneous graphs with causality of event types. Int J Crowd Sci 6(2):80\u201391","journal-title":"Int J Crowd Sci"},{"issue":"3","key":"439_CR31","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TPDS.2019.2939126","volume":"31","author":"B Zarebavani","year":"2019","unstructured":"Zarebavani B, Jafarinejad F, Hashemi M, Salehkaleybar S (2019) cupc: Cuda-based parallel pc algorithm for causal structure learning on gpu. IEEE Trans Parallel Distrib Syst 31(3):530\u2013542","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"5","key":"439_CR32","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/TCBB.2016.2591526","volume":"16","author":"TD Le","year":"2016","unstructured":"Le TD, Hoang T, Li J, Liu L, Liu H, Shu H (2016) A fast pc algorithm for high dimensional causal discovery with multi-core pcs. IEEE\/ACM Trans Comput Biol Bioinf 16(5):1483\u20131495","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"439_CR33","doi-asserted-by":"crossref","unstructured":"Huang B, Zhang K, Lin Y, Sch\u00f6lkopf Bernhard, Glymour Clark (2018) Generalized score functions for causal discovery. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 1551\u20131560","DOI":"10.1145\/3219819.3220104"},{"issue":"2","key":"439_CR34","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1002\/wics.199","volume":"4","author":"AA Neath","year":"2012","unstructured":"Neath AA, Cavanaugh JE (2012) The bayesian information criterion: background, derivation, and applications. Wiley Interdisciplinary Rev: Comput Stat 4(2):199\u2013203","journal-title":"Wiley Interdisciplinary Rev: Comput Stat"},{"issue":"11","key":"439_CR35","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.1109\/TPAMI.2016.2636828","volume":"39","author":"H Amirkhani","year":"2016","unstructured":"Amirkhani H, Rahmati M, Lucas PJF, Hommersom A (2016) Exploiting experts\u2019 knowledge for structure learning of Bayesian networks. IEEE Trans Pattern Anal Mach Intell 39(11):2154\u20132170","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"439_CR36","unstructured":"Ban T, Chen L, Wang X, Chen H (2023) From query tools to causal architects: Harnessing large language models for advanced causal discovery from data. arXiv preprint arXiv:2306.16902"},{"key":"439_CR37","doi-asserted-by":"crossref","unstructured":"Wang X, Ban T, Chen L, Usman M, Guan Y, Lyu D, Cheng J, Chen H, Leung C, Miao C (2023) Decentralised knowledge graph evolution via blockchain. IEEE Transactions on Services Computing","DOI":"10.1109\/TSC.2023.3337873"},{"issue":"1","key":"439_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/JDM.318456","volume":"34","author":"T Ban","year":"2023","unstructured":"Ban T, Wang X, Wang X, Zhu J, Chen L, Fan Y (2023) Knowledge extraction from national standards for natural resources: a method for multi-domain texts. J Database Manage (JDM) 34(1):1\u201323","journal-title":"J Database Manage (JDM)"},{"key":"439_CR39","unstructured":"Chen L, Ban T, Wang X, Lyu D, Chen H (2023) Mitigating prior errors in causal structure learning: towards llm driven prior knowledge. arXiv preprint  arXiv:2306.07032"},{"key":"439_CR40","unstructured":"Ban T, Chen L, Lyu D, Wang X, Chen H (2023) Causal structure learning supervised by large language model. arXiv preprint  arXiv:2311.11689"},{"key":"439_CR41","unstructured":"Wang X, Ban T, Chen L, Wu X, Lyu D, Chen H (2022) Knowledge verification from data. IEEE Transactions on Neural Networks and Learning Systems, pages 1\u201315,"},{"key":"439_CR42","unstructured":"Ban T, Wang X, Chen L, Wu X, Chen Q, Chen H (2022) Quality evaluation of triples in knowledge graph by incorporating internal with external consistency. IEEE Transactions on Neural Networks and Learning Systems"},{"key":"439_CR43","doi-asserted-by":"crossref","unstructured":"Wang X, Ban T, Chen L, Usman M, Wu T, Chen Q, Chen H (2023) A distribution-based representation of knowledge quality. Knowledge-Based Systems, page 111054","DOI":"10.1016\/j.knosys.2023.111054"},{"key":"439_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106515","volume":"211","author":"Z Wang","year":"2021","unstructured":"Wang Z, Xiaoguang Gao Yu, Yang XT, Chen D (2021) Learning Bayesian networks based on order graph with ancestral constraints. Knowl-Based Syst 211:106515","journal-title":"Knowl-Based Syst"},{"key":"439_CR45","doi-asserted-by":"crossref","unstructured":"Patr\u00edcio M, Pereira JA Lobo, Cris\u00f3stomo J, Matafome P, Gomes MM, Sei\u00e7a R, Caramelo F (2018) Using resistin, glucose, age and bmi to predict the presence of breast cancer. BMC Cancer, 18","DOI":"10.1186\/s12885-017-3877-1"},{"key":"439_CR46","doi-asserted-by":"crossref","unstructured":"Alldredge J, Leaf MC, Patel P, Coakley K, Longoria T, McLaren C, Randall Leslie M (2020) Prevalence and predictors of hiv screening in invasive cervical cancer: a 10 year cohort study. International J Gynecol Cancer 30(6)","DOI":"10.1136\/ijgc-2019-000909"},{"issue":"Nov","key":"439_CR47","first-page":"507","volume":"3","author":"DM Chickering","year":"2002","unstructured":"Chickering DM (2002) Optimal structure identification with greedy search. J Mach Learn Res 3(Nov):507\u2013554","journal-title":"J Mach Learn Res"},{"key":"439_CR48","doi-asserted-by":"crossref","unstructured":"G\u00e1mez Jos\u00e9 A, Mateo Juan L, Puerta Jos\u00e9 M (2007) A fast hill-climbing algorithm for bayesian networks structure learning. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 9th European Conference, ECSQARU 2007, Hammamet, Tunisia, October 31-November 2, 2007. Proceedings 9, pages 585\u2013597. Springer","DOI":"10.1007\/978-3-540-75256-1_52"}],"container-title":["Memetic Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-025-00439-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12293-025-00439-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-025-00439-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T01:29:42Z","timestamp":1742261382000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12293-025-00439-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,22]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["439"],"URL":"https:\/\/doi.org\/10.1007\/s12293-025-00439-5","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4441701\/v1","asserted-by":"object"}]},"ISSN":["1865-9284","1865-9292"],"issn-type":[{"value":"1865-9284","type":"print"},{"value":"1865-9292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,22]]},"assertion":[{"value":"18 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"7"}}