{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:41:54Z","timestamp":1761198114229,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T00:00:00Z","timestamp":1664496000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T00:00:00Z","timestamp":1664496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972274","61872261"],"award-info":[{"award-number":["61972274","61872261"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011160","name":"State Key Laboratory of Virtual Reality Technology and Systems","doi-asserted-by":"publisher","award":["VRLAB2020B06"],"award-info":[{"award-number":["VRLAB2020B06"]}],"id":[{"id":"10.13039\/501100011160","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013317","name":"Shanxi Provincial Key Research and Development Project","doi-asserted-by":"publisher","award":["201903D321034"],"award-info":[{"award-number":["201903D321034"]}],"id":[{"id":"10.13039\/501100013317","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s13042-022-01671-y","type":"journal-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T15:05:36Z","timestamp":1664550336000},"page":"911-927","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Attention Matching Network for few-shot learning in the syndrome differentiation of cerebral stroke"],"prefix":"10.1007","volume":"14","author":[{"given":"Zijuan","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Kai","family":"Song","sequence":"additional","affiliation":[]},{"given":"Xueting","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Qiang","sequence":"additional","affiliation":[]},{"given":"Juanjuan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jiaxin","family":"Hou","sequence":"additional","affiliation":[]},{"given":"Junyi","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Ning","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Junlong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,30]]},"reference":[{"key":"1671_CR1","doi-asserted-by":"publisher","unstructured":"Chinese Society of Neurology, Cerebrovascular Group (2018) Chinese Guidelines for the diagnosis and treatment of acute ischemic stroke 2018. Chin J Neurol (in Chinese) 51(9):666\u2013682. https:\/\/doi.org\/10.3760\/cma.j.issn.1006-7876.2018.09.004","DOI":"10.3760\/cma.j.issn.1006-7876.2018.09.004"},{"issue":"5","key":"1671_CR2","first-page":"28","volume":"6","author":"S Chen","year":"2015","unstructured":"Chen S, Yang F (2015) Characteristics of ischemic stroke in young adults. Renown Doc (in Chinese) 6(5):28\u201329","journal-title":"Renown Doc (in Chinese)"},{"key":"1671_CR3","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1328\/j.11-2166\/r.2021.16.009","volume":"16","author":"L Zhang","year":"2021","unstructured":"Zhang L, Xie Y, Gao Y, Wei R (2021) Correlation between traditional Chinese medicine syndromes and constitutions in 2558 patients with ischemic stroke. J Tradit Chin Med (in Chinese) 16:1416\u20131420. https:\/\/doi.org\/10.1328\/j.11-2166\/r.2021.16.009","journal-title":"J Tradit Chin Med (in Chinese)"},{"issue":"11","key":"1671_CR4","doi-asserted-by":"publisher","first-page":"695","DOI":"10.21037\/atm-2020-mair-21","volume":"8","author":"H Lin","year":"2020","unstructured":"Lin H, Yu L (2020) Medical artificial intelligent research: translating artificial intelligence into clinical practice [J]. Ann Transl Med 8(11):695\u2013695","journal-title":"Ann Transl Med"},{"key":"1671_CR5","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/936290","author":"Y Zhao","year":"2015","unstructured":"Zhao Y, He L, Xie Q, Li G, Liu B, Wang J, Zhang X, Zhang X, Luo L, Li K, Jing X (2015) A novel classification method for syndrome differentiation of patients with AIDS. Evid Based Complement Alternat Med. https:\/\/doi.org\/10.1155\/2015\/936290","journal-title":"Evid Based Complement Alternat Med"},{"key":"1671_CR6","volume-title":"RETAIN: interpretable predictive model in healthcare using reverse time attention mechanism [C]","author":"E Choi","year":"2016","unstructured":"Choi E, Bahadori MT, Schuetz A et al (2016) RETAIN: interpretable predictive model in healthcare using reverse time attention mechanism [C]. Curran Associates Inc., Red Hook"},{"key":"1671_CR7","doi-asserted-by":"crossref","unstructured":"Liu Z, Li X, Peng H et al (2021) Heterogeneous similarity graph neural network on electronic health records [C]. In: International conference on big data. IEEE","DOI":"10.1109\/BigData50022.2020.9377795"},{"key":"1671_CR8","doi-asserted-by":"crossref","unstructured":"Singla J, Kaur B (2021) 2 A medical intelligent system for diagnosis of chronic kidney disease using adaptive neuro-fuzzy inference system [J]","DOI":"10.1515\/9783110676112-002"},{"issue":"3","key":"1671_CR9","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.artmed.2007.10.004","volume":"42","author":"NL Zhang","year":"2008","unstructured":"Zhang NL, Yuan S, Chen T, Wang Y (2008) Latent tree models and diagnosis in traditional Chinese medicine. Artif Intell Med 42(3):229\u2013245. https:\/\/doi.org\/10.1016\/j.artmed.2007.10.004","journal-title":"Artif Intell Med"},{"key":"1671_CR10","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1007\/978-3-319-95933-7_55","volume-title":"International conference on intelligent computing","author":"N Zhou","year":"2018","unstructured":"Zhou N, Zhou L, Peng L, Wang B, Chen P, Zhang J (2018) Verifying TCM syndrome hypothesis based on improved latent tree model. International conference on intelligent computing. Springer, Cham, pp 460\u2013469. https:\/\/doi.org\/10.1007\/978-3-319-95933-7_55"},{"issue":"3","key":"1671_CR11","first-page":"688","volume":"27","author":"L Chen","year":"2016","unstructured":"Chen L, Wang X (2016) Summary of diagnosis model of TCM syndrome. Lishizhen Med Mater Res (in Chinese) 27(3):688\u2013690","journal-title":"Lishizhen Med Mater Res (in Chinese)"},{"key":"1671_CR12","doi-asserted-by":"publisher","unstructured":"Lu Z, Guang-geng L, Yu-mei Z, Dan Y, Yan, Sun (2018) Traditional Chinese Medicine (TCM) diagnosis model building based on multi-label classification. In: International conference on electronic information technology & computer engineering. https:\/\/doi.org\/10.1051\/matecconf\/201823202026","DOI":"10.1051\/matecconf\/201823202026"},{"key":"1671_CR13","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/938350","author":"G Liu","year":"2014","unstructured":"Liu G, Yan J, Wang Y, Zheng W, Zhong T, Lu X, Qian P (2014) Deep learning based syndrome diagnosis of chronic gastritis. Comput Math Methods Med. https:\/\/doi.org\/10.1155\/2014\/938350","journal-title":"Comput Math Methods Med"},{"key":"1671_CR14","doi-asserted-by":"publisher","first-page":"76167","DOI":"10.1109\/ACCESS.2019.2921318","volume":"7","author":"Q Xu","year":"2019","unstructured":"Xu Q, Tang W, Teng F, Peng W, Zhang Y, Li W, Wen C, Guo J (2019) Intelligent syndrome differentiation of traditional chinese medicine by ANN: a case study of chronic obstructive pulmonary disease. IEEE Access 7:76167\u201376175. https:\/\/doi.org\/10.1109\/ACCESS.2019.2921318","journal-title":"IEEE Access"},{"key":"1671_CR15","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.cmpb.2018.10.011","volume":"174","author":"Q Hu","year":"2019","unstructured":"Hu Q, Yu T, Li J, Yu Q, Zhu L, Gu Y (2019) End-to-End syndrome differentiation of Yin deficiency and Yang deficiency in traditional Chinese medicine. Comput Methods Progr Biomed 174:9\u201315. https:\/\/doi.org\/10.1016\/j.cmpb.2018.10.011","journal-title":"Comput Methods Progr Biomed"},{"key":"1671_CR16","doi-asserted-by":"publisher","DOI":"10.2196\/17608","author":"H Zhang","year":"2020","unstructured":"Zhang H, Ni W, Li J, Zhang J (2020) Artificial intelligence-based traditional chinese medicine assistive diagnostic system: validation study. JMIR Med Inf. https:\/\/doi.org\/10.2196\/17608","journal-title":"JMIR Med Inf"},{"key":"1671_CR17","doi-asserted-by":"publisher","DOI":"10.2196\/17821","author":"Z Liu","year":"2020","unstructured":"Liu Z, He H, Yan S, Wang Y, Yang T, Li G-Z (2020) End-to-end models to imitate traditional chinese medicine syndrome differentiation in lung cancer diagnosis: model development and validation. JMIR Med Inf. https:\/\/doi.org\/10.2196\/17821","journal-title":"JMIR Med Inf"},{"key":"1671_CR18","doi-asserted-by":"publisher","unstructured":"Feifei L, Fergus R, Perona P (2003) A Bayesian approach to unsupervised one-shot learning of object categories. In: IEEE international conference on computer vision, pp 1134\u20131141. https:\/\/doi.org\/10.1109\/ICCV.2003.1238476","DOI":"10.1109\/ICCV.2003.1238476"},{"issue":"4","key":"1671_CR19","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","volume":"28","author":"L Feifei","year":"2006","unstructured":"Feifei L, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28(4):594\u2013611. https:\/\/doi.org\/10.1109\/TPAMI.2006.79","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1671_CR20","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning"},{"issue":"4","key":"1671_CR21","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1177\/1536867X1501500403","volume":"15","author":"IR White","year":"2015","unstructured":"White IR (2015) Network meta-analysis. Stata J 15(4):951\u2013985. https:\/\/doi.org\/10.1177\/1536867X1501500403","journal-title":"Stata J"},{"issue":"11","key":"1671_CR22","doi-asserted-by":"publisher","first-page":"6844","DOI":"10.1109\/TGRS.2014.2303895","volume":"52","author":"B Du","year":"2014","unstructured":"Du B, Zhang L (2014) A discriminative metric learning based anomaly detection method. IEEE Trans Geosci Remote Sens 52(11):6844\u20136857. https:\/\/doi.org\/10.1109\/TGRS.2014.2303895","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1671_CR23","unstructured":"Ravi S, Larochelle H (2017) Optimization as a model for few-shot learning. In: International conference on learning representations"},{"key":"1671_CR24","unstructured":"Mishra N, Rohaninejad M, Chen X, Abbeel P (2017) A simple neural attentive meta-learner. In: International conference on learning representations"},{"issue":"5","key":"1671_CR25","doi-asserted-by":"publisher","first-page":"2509","DOI":"10.1109\/TGRS.2016.2645703","volume":"55","author":"Y Dong","year":"2017","unstructured":"Dong Y, Du B, Zhang L, Zhang L (2017) Dimensionality reduction and classification of hyperspectral images using ensemble discriminative local metric learning. IEEE Trans Geosci Remote Sens 55(5):2509\u20132524. https:\/\/doi.org\/10.1109\/TGRS.2016.2645703","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1671_CR26","unstructured":"Koch G, Zemel R, Salakhutdinov R (2015) Siamese neural networks for one-shot image recognition. In: The 32nd international conference on machine learning"},{"key":"1671_CR27","unstructured":"Vinyals O, Blundell C, Lillicrap T, Kavukcuoglu K, Wierstra D (2016) Matching networks for one shot learning. In: Proceedings of the 30th international conference on neural information processing systems. Curran Associates Inc., pp 3637\u20133645. http:\/\/arxiv.org\/abs\/1606.04080"},{"key":"1671_CR28","doi-asserted-by":"publisher","unstructured":"Wang J, Zhai Y (2020) Prototypical siamese networks for few-shot learning. In: 2020 IEEE 10th International conference on electronics information and emergency communication (ICEIEC), IEEE, pp 178\u2013181. https:\/\/doi.org\/10.1109\/ICEIEC49280.2020.9152261","DOI":"10.1109\/ICEIEC49280.2020.9152261"},{"key":"1671_CR29","doi-asserted-by":"publisher","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PHS, Hospedales TM and Ieee (2018) Learning to compare: relation network for few-shot learning. In: IEEE international conference on computer vision, pp 1199\u20131208. https:\/\/doi.org\/10.1109\/CVPR.2018.00131","DOI":"10.1109\/CVPR.2018.00131"},{"key":"1671_CR30","doi-asserted-by":"crossref","unstructured":"Geng R, Li B, Li Y, Sun J, Zhu X, Assoc Computat L (2020) Dynamic memory induction networks for few-shot text classification. In: 58th Annual meeting of the association for computational linguistics, pp 1087\u20131094","DOI":"10.18653\/v1\/2020.acl-main.102"},{"key":"1671_CR31","doi-asserted-by":"crossref","unstructured":"Yu M, Guo X, Yi J, Chang S, Potdar S, Cheng Y, Tesauro G, Wang H, Zhou BJ (2018) Diverse few-shot text classification with multiple metrics. https:\/\/arxiv.org\/pdf\/1805.07513.pdf","DOI":"10.18653\/v1\/N18-1109"},{"key":"1671_CR32","first-page":"2204","volume":"3","author":"V Mnih","year":"2014","unstructured":"Mnih V, Heess N, Graves A, Kavukcuoglu K (2014) Recurrent models of visual attention. Adv Neural Inf Process Syst 3:2204\u20132212","journal-title":"Adv Neural Inf Process Syst"},{"key":"1671_CR33","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. https:\/\/arxiv.org\/pdf\/1409.0473.pdf"},{"key":"1671_CR34","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems. Curran Associates Inc., pp 6000\u20136010. http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"1671_CR35","doi-asserted-by":"publisher","unstructured":"Kiyono S, Suzuki J, Mizumoto T, Inui K (2020) Massive exploration of pseudo data for grammatical error correction. In: IEEE\/ACM transactions on audio, speech, and language processing, pp 2134\u20132145. https:\/\/doi.org\/10.1109\/TASLP.2020.3007753","DOI":"10.1109\/TASLP.2020.3007753"},{"key":"1671_CR36","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00244","author":"W Yin","year":"2015","unstructured":"Yin W, Sch\u00fctze H, Xiang B, Zhou B (2015) ABCNN: attention-based convolutional neural network for modeling sentence pairs. Trans Assoc Comput Linguist. https:\/\/doi.org\/10.1162\/tacl_a_00244","journal-title":"Trans Assoc Comput Linguist"},{"key":"1671_CR37","doi-asserted-by":"publisher","unstructured":"P. Zhuang, Y. Wan, Y. Qiao and I. Assoc Advancement Artificial. Learning Attentive Pairwise Interaction for Fine-Grained Classification, The AAAI Conference on Artificial Intelligence, 2020. 13130\u201313137. https:\/\/doi.org\/10.1609\/aaai.v34i07.7016","DOI":"10.1609\/aaai.v34i07.7016"},{"key":"1671_CR38","doi-asserted-by":"publisher","unstructured":"Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: The 54th annual meeting of the association for computational linguistics, pp 207\u2013212. https:\/\/doi.org\/10.18653\/v1\/p16-2034","DOI":"10.18653\/v1\/p16-2034"},{"key":"1671_CR39","doi-asserted-by":"publisher","unstructured":"Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based LSTM for aspect-level sentiment classification. In: The 2016 conference on empirical methods in natural language processing, pp 606\u2013615. https:\/\/doi.org\/10.18653\/v1\/D16-1058","DOI":"10.18653\/v1\/D16-1058"},{"key":"1671_CR40","doi-asserted-by":"crossref","unstructured":"Ma Y, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: The AAAI conference on artificial intelligence, pp 5876\u20135883","DOI":"10.1609\/aaai.v32i1.12048"},{"issue":"2","key":"1671_CR41","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s13042-020-01176-6","volume":"12","author":"T Zhang","year":"2021","unstructured":"Zhang T, Lin H, Tadesse MM, Ren Y, Duan X, Xu B (2021) Chinese medical relation extraction based on multi-hop self-attention mechanism. Int J Mach Learn Cybern 12(2):355\u2013363. https:\/\/doi.org\/10.1007\/s13042-020-01176-6","journal-title":"Int J Mach Learn Cybern"},{"key":"1671_CR42","doi-asserted-by":"publisher","first-page":"30548","DOI":"10.1109\/ACCESS.2019.2954985","volume":"8","author":"Y Dong","year":"2020","unstructured":"Dong Y, Liu P, Zhu Z, Wang Q, Zhang Q (2020) A fusion model-based label embedding and self-interaction attention for text classification. IEEE Access 8:30548\u201330559. https:\/\/doi.org\/10.1109\/ACCESS.2019.2954985","journal-title":"IEEE Access"},{"key":"1671_CR43","unstructured":"General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China (2006) Standardization Administration of China. Basic theory nomenclature of traditional Chinese medicine. http:\/\/c.gb688.cn\/bzgk\/gb\/showGb?type=online&hcno=EFB5E3CEF5147682E9678C7F9DA2CBDE"},{"key":"1671_CR44","doi-asserted-by":"crossref","unstructured":"Wang Z, Poon J, Sun S, Poon S (2018) CNN based multi-instance multi-task learning for syndrome differentiation of diabetic patients. In: IEEE international conference on bioinformatics and biomedicine, pp 1905\u20131911. http:\/\/arxiv.org\/abs\/1812.07764","DOI":"10.1109\/BIBM.2018.8621344"},{"key":"1671_CR45","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation, conference on empirical methods in natural language processing","DOI":"10.3115\/v1\/D14-1162"},{"key":"1671_CR46","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121\u20132159. http:\/\/dl.acm.org\/citation.cfm?id=2021068"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01671-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01671-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01671-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T03:49:06Z","timestamp":1677037746000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01671-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,30]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["1671"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01671-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2022,9,30]]},"assertion":[{"value":"21 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and\/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled Attention Matching Network for few-shot learning in the syndrome differentiation of cerebral stroke.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}