{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:11:17Z","timestamp":1781687477020,"version":"3.54.5"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031263897","type":"print"},{"value":"9783031263903","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":75,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>An adverse drug event (ADE) is defined as an adverse reaction resulting from improper drug use, reported in various documents such as biomedical literature, drug reviews, and user posts on social media. The recent advances in natural language processing techniques have facilitated automated ADE detection from documents. However, the contextualized information and relations among text pieces are less explored. This paper investigates contextualized language models and heterogeneous graph representations. It builds a contextualized graph embedding model for adverse drug event detection. We employ different convolutional graph neural networks and pre-trained contextualized embeddings as the building blocks. Experimental results show that our methods can improve the performance by comparing recent ADE detection models, suggesting that a text graph can capture causal relationships and dependency between different entities in a document.<\/jats:p>","DOI":"10.1007\/978-3-031-26390-3_35","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T09:04:46Z","timestamp":1678957486000},"page":"605-620","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Contextualized Graph Embeddings for\u00a0Adverse Drug Event Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4056-679X","authenticated-orcid":false,"given":"Ya","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3281-8002","authenticated-orcid":false,"given":"Shaoxiong","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongxuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2851-4260","authenticated-orcid":false,"given":"Prayag","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7078-7927","authenticated-orcid":false,"given":"Pekka","family":"Marttinen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"35_CR1","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/978-3-030-01204-5_18","volume-title":"Artificial Intelligence and Natural Language","author":"I Alimova","year":"2018","unstructured":"Alimova, I., Solovyev, V.: Interactive attention network for adverse drug reaction classification. In: Ustalov, D., Filchenkov, A., Pivovarova, L., \u017di\u017eka, J. (eds.) AINL 2018. CCIS, vol. 930, pp. 185\u2013196. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01204-5_18"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Alsentzer, E., et al.: Publicly available clinical BERT embeddings. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 72\u201378 (2019)","DOI":"10.18653\/v1\/W19-1909"},{"issue":"2","key":"35_CR3","doi-asserted-by":"publisher","DOI":"10.2196\/publichealth.6396","volume":"3","author":"N Alvaro","year":"2017","unstructured":"Alvaro, N., Miyao, Y., Collier, N.: Twimed: Twitter and PubMed comparable corpus of drugs, diseases, symptoms, and their relations. JMIR Public Health Surveill. 3(2), e6396 (2017)","journal-title":"JMIR Public Health Surveill."},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Bollegala, D., Sloane, R., Maskell, S., Hajne, J., Pirmohamed, M.: Learning causality patterns for detecting adverse drug reactions from social media. J. Med. Internet Res. (2018)","DOI":"10.2196\/preprints.8214"},{"issue":"4","key":"35_CR5","first-page":"813","volume":"24","author":"A Cocos","year":"2017","unstructured":"Cocos, A., Fiks, A.G., Masino, A.J.: Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in twitter posts. JAMIA 24(4), 813\u2013821 (2017)","journal-title":"JAMIA"},{"key":"35_CR6","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (2019)"},{"key":"35_CR7","unstructured":"Donaldson, M.S., Corrigan, J.M., Kohn, L.T., et al.: To Err is Human: Building a Safer Health System (2000)"},{"issue":"2","key":"35_CR8","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1109\/TITB.2012.2227272","volume":"17","author":"L Duan","year":"2012","unstructured":"Duan, L., Khoshneshin, M., Street, W.N., Liu, M.: Adverse drug effect detection. IEEE J. Biomed. Health Inform. 17(2), 305\u2013311 (2012)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Ge, S., Qi, T., Wu, C., Huang, Y.: Detecting and extracting of adverse drug reaction mentioning tweets with multi-head self attention. In: Proceedings of SMM4H Workshop, pp. 96\u201398 (2019)","DOI":"10.18653\/v1\/W19-3214"},{"key":"35_CR10","unstructured":"Huynh, T., He, Y., Willis, A., R\u00fcger, S.: Adverse drug reaction classification with deep neural networks. In: COLING (2016)"},{"key":"35_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104998","volume":"139","author":"S Ji","year":"2021","unstructured":"Ji, S., H\u00f6ltt\u00e4, M., Marttinen, P.: Does the magic of BERT apply to medical code assignment? A quantitative study. Comput. Biol. Med. 139, 104998 (2021)","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"35_CR12","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1109\/TCBB.2020.2979959","volume":"18","author":"T Jiang","year":"2020","unstructured":"Jiang, T., et al.: Biomedical knowledge graphs construction from conditional statements. IEEE\/ACM Trans. Comput. Biol. Bioinf. 18(3), 823\u2013835 (2020)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"1","key":"35_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson, A.E., et al.: Mimic-iii, a freely accessible critical care database. Sci. Data 3(1), 1\u20139 (2016)","journal-title":"Sci. Data"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Kayastha, T., Gupta, P., Bhattacharyya, P.: BERT based adverse drug effect tweet classification. In: Proceedings of SMM4H Workshop, pp. 88\u201390 (2021)","DOI":"10.18653\/v1\/2021.smm4h-1.15"},{"key":"35_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"35_CR16","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)"},{"issue":"4","key":"35_CR17","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"issue":"2","key":"35_CR18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0089829","volume":"9","author":"H Li","year":"2014","unstructured":"Li, H., et al.: Adverse drug reactions of spontaneous reports in shanghai pediatric population. PLoS ONE 9(2), e89829 (2014)","journal-title":"PLoS ONE"},{"key":"35_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103431","volume":"106","author":"Z Li","year":"2020","unstructured":"Li, Z., Yang, Z., Luo, L., Xiang, Y., Lin, H.: Exploiting adversarial transfer learning for adverse drug reaction detection from texts. J. Biomed. Inform. 106, 103431 (2020)","journal-title":"J. Biomed. Inform."},{"key":"35_CR20","doi-asserted-by":"crossref","unstructured":"Lin, Y., et al.: BertGCN: Transductive Text Classification by Combining GCN and BERT. arXiv preprint arXiv:2105.05727 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.126"},{"key":"35_CR21","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Magge, A., et al.: Overview of the sixth social media mining for health applications (# smm4h) shared tasks at NAACL 2021. In: Proceedings of SMM4H Workshop, pp. 21\u201332 (2021)","DOI":"10.18653\/v1\/2021.smm4h-1.4"},{"key":"35_CR23","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., Vu, T., Nguyen, A.T.: BERTweet: a pre-trained language model for english tweets. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 9\u201314 (2020)","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"35_CR24","unstructured":"Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)"},{"key":"35_CR25","doi-asserted-by":"crossref","unstructured":"Pimpalkhute, V., Nakhate, P., Diwan, T.: IIITN NLP at SMM4H 2021 tasks: transformer models for classification on health-related imbalanced twitter datasets. In: Proceedings of SMM4H Workshop, pp. 118\u2013122 (2021)","DOI":"10.18653\/v1\/2021.smm4h-1.24"},{"key":"35_CR26","unstructured":"Sarker, A., Gonzalez-Hernandez, G.: Overview of the second social media mining for health (SMM4H) shared tasks at AMIA 2017. Training. 1(10,822), 1239 (2017)"},{"issue":"2","key":"35_CR27","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/S0378-3758(00)00115-4","volume":"90","author":"H Shimodaira","year":"2000","unstructured":"Shimodaira, H.: Improving predictive inference under covariate shift by weighting the log-likelihood function. J. Statist. Plann. Inference 90(2), 227\u2013244 (2000)","journal-title":"J. Statist. Plann. Inference"},{"issue":"5","key":"35_CR28","first-page":"858","volume":"21","author":"S Sohn","year":"2014","unstructured":"Sohn, S., Clark, C., Halgrim, S.R., Murphy, S.P., Chute, C.G., Liu, H.: MedXN: an open source medication extraction and normalization tool for clinical text. JAMIA 21(5), 858\u2013865 (2014)","journal-title":"JAMIA"},{"issue":"Suppl1","key":"35_CR29","doi-asserted-by":"publisher","first-page":"S73","DOI":"10.4103\/0976-500X.120957","volume":"4","author":"J Sultana","year":"2013","unstructured":"Sultana, J., Cutroneo, P., Trifir\u00f2, G.: Clinical and economic burden of adverse drug reactions. J. Pharmacol. Pharmacotherap. 4(Suppl1), S73 (2013)","journal-title":"J. Pharmacol. Pharmacotherap."},{"key":"35_CR30","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)"},{"issue":"1","key":"35_CR31","first-page":"13","volume":"27","author":"Q Wei","year":"2020","unstructured":"Wei, Q., et al.: A study of deep learning approaches for medication and adverse drug event extraction from clinical text. JAMIA 27(1), 13\u201321 (2020)","journal-title":"JAMIA"},{"key":"35_CR32","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F., Liu, J., Wu, S., Huang, Y., Xie, X.: Detecting tweets mentioning drug name and adverse drug reaction with hierarchical tweet representation and multi-head self-attention. In: Proceedings of SMM4H Workshop, pp. 34\u201337 (2018)","DOI":"10.18653\/v1\/W18-5909"},{"issue":"1","key":"35_CR33","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"35_CR34","doi-asserted-by":"crossref","unstructured":"Wunnava, S., Qin, X., Kakar, T., Kong, X., Rundensteiner, E.: A dual-attention network for joint named entity recognition and sentence classification of adverse drug events. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pp. 3414\u20133423 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.306"},{"key":"35_CR35","doi-asserted-by":"crossref","unstructured":"Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of AAAI, vol. 33, pp. 7370\u20137377 (2019)","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"35_CR36","doi-asserted-by":"crossref","unstructured":"Yaseen, U., Langer, S.: Neural text classification and stacked heterogeneous embeddings for named entity recognition in SMM4H 2021. In: Proceedings of SMM4H Workshop, pp. 83\u201387 (2021)","DOI":"10.18653\/v1\/2021.smm4h-1.14"},{"issue":"1","key":"35_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3053-5","volume":"20","author":"T Zhang","year":"2019","unstructured":"Zhang, T., et al.: Adverse drug reaction detection via a multihop self-attention mechanism. BMC Bioinform. 20(1), 1\u201311 (2019)","journal-title":"BMC Bioinform."},{"key":"35_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, T., et al.: Gated iterative capsule network for adverse drug reaction detection from social media. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 387\u2013390. IEEE (2020)","DOI":"10.1109\/BIBM49941.2020.9313092"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26390-3_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T07:11:06Z","timestamp":1697181066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26390-3_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263897","9783031263903"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26390-3_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"236","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}