{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T01:05:40Z","timestamp":1779325540856,"version":"3.51.4"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032214768","type":"print"},{"value":"9783032214775","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-21477-5_19","type":"book-chapter","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:18:52Z","timestamp":1779322732000},"page":"286-296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Interactive Dashboard for\u00a0Exploring Patient-Reported Drug-Condition Relations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4498-2769","authenticated-orcid":false,"given":"Vanni","family":"Zavarella","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1709-9372","authenticated-orcid":false,"given":"Lorenzo","family":"Bertolini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7357-5858","authenticated-orcid":false,"given":"Sergio","family":"Consoli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4668-2476","authenticated-orcid":false,"given":"Gianni","family":"Fenu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8646-6183","authenticated-orcid":false,"given":"Diego Reforgiato","family":"Recupero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandro","family":"Zani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103820","volume":"119","author":"A Akkasi","year":"2021","unstructured":"Akkasi, A., Moens, M.F.: Causal relationship extraction from biomedical text using deep neural models: a comprehensive survey. J. Biomed. Inform. 119, 103820 (2021). https:\/\/doi.org\/10.1016\/j.jbi.2021.103820","journal-title":"J. Biomed. Inform."},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Cui, S., Jin, Z., Sch\u00f6lkopf, B., Faltings, B.: The odyssey of commonsense causality: from foundational benchmarks to cutting-edge reasoning (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.932"},{"issue":"2","key":"19_CR3","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/s11135-014-0003-1","volume":"49","author":"R Falotico","year":"2015","unstructured":"Falotico, R., Quatto, P.: Fleiss\u2019 kappa statistic without paradoxes. Qual. Quant. 49(2), 463\u2013470 (2015). https:\/\/doi.org\/10.1007\/s11135-014-0003-1","journal-title":"Qual. Quant."},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Fernainy, P., Cohen, A., et\u00a0al., M.E.: Rethinking the pros and cons of randomized controlled trials and observational studies in the era of big data and advanced methods: a panel discussion. BMC Proc. 18(Suppl. 2) (2024)","DOI":"10.1186\/s12919-023-00285-8"},{"issue":"5","key":"19_CR5","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1016\/j.jbi.2012.04.008","volume":"45","author":"H Gurulingappa","year":"2012","unstructured":"Gurulingappa, H., Rajput, A.M., Roberts, A., Fluck, J., Hofmann-Apitius, M., Toldo, L.: Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. J. Biomed. Inform. 45(5), 885\u2013892 (2012)","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"19_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-79432-2","volume":"1","author":"T Heath","year":"2011","unstructured":"Heath, T., Bizer, C.: Linked data: evolving the web into a global data space. Synthesis Lectures on the Semantic Web: Theory and Technology 1(1), 1\u2013121 (2011)","journal-title":"Synthesis Lectures on the Semantic Web: Theory and Technology"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: Mimic-iii, a freely accessible critical care database. Scientific Data 3(1), 1\u20139 (2016)","DOI":"10.1038\/sdata.2016.35"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Khetan, V., Rizvi, M.I.H., Huber, J., Bartusiak, P., Sacaleanu, B., Fano, A.: MIMICause: representation and automatic extraction of causal relation types from clinical notes. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 764 \u2013 773 (2022)","DOI":"10.18653\/v1\/2022.findings-acl.63"},{"key":"19_CR9","doi-asserted-by":"publisher","unstructured":"McHugh, M.L.: Interrater reliability: The kappa statistic. Biochemia Medica 22(3), 276 \u2013 282 (2012). https:\/\/doi.org\/10.11613\/bm.2012.031","DOI":"10.11613\/bm.2012.031"},{"key":"19_CR10","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/1471-2105-14-2","volume":"14","author":"C Mihaila","year":"2013","unstructured":"Mihaila, C., Ohta, T., Pyysalo, S., Ananiadou, S.: BioCause: annotating and analysing causality in the biomedical domain. BMC Bioinform. 14, 2 (2013)","journal-title":"BMC Bioinform."},{"key":"19_CR11","unstructured":"Mozer, R., Kaufman, A.R., Celi, L.A., Miratrix, L.: Leveraging text data for causal inference using electronic health records (2024)"},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.jbi.2017.05.021","volume":"71","author":"C Ochs","year":"2017","unstructured":"Ochs, C., Perl, Y., Geller, J., Arabandi, S., Tudorache, T., Musen, M.A.: An empirical analysis of ontology reuse in BioPortal. J. Biomed. Inform. 71, 165\u2013177 (2017). https:\/\/doi.org\/10.1016\/j.jbi.2017.05.021","journal-title":"J. Biomed. Inform."},{"key":"19_CR13","doi-asserted-by":"publisher","unstructured":"Rinaldi, F., et al.: BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language. Database 2016, baw067 (2016). https:\/\/doi.org\/10.1093\/database\/baw067","DOI":"10.1093\/database\/baw067"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Shen, X., Ma, S., Vemuri, P., Castro, M.R., Caraballo, P.J., Simon, G.J.: A novel method for causal structure discovery from EHR data and its application to type-2 diabetes mellitus. Sci. Rep. 11(1) (2021)","DOI":"10.1038\/s41598-021-99990-7"},{"issue":"2","key":"19_CR15","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s41237-022-00173-z","volume":"49","author":"S Shimizu","year":"2022","unstructured":"Shimizu, S., Kawano, S.: Special issue: recent developments in causal inference and machine learning. Behaviormetrika 49(2), 275\u2013276 (2022)","journal-title":"Behaviormetrika"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Whetzel, P.L., et al.: Bioportal: enhanced functionality via new web services from the national center for biomedical ontology to access and use ontologies in software applications. Nucleic Acids Res. 39(suppl_2), W541\u2013W545 (2011)","DOI":"10.1093\/nar\/gkr469"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., et\u00a0al.: Transformers: State-of-the-art natural language processing. In: Liu, Q., Schlangen, D. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38\u201345. Association for Computational Linguistics, Online, October 2020","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"19_CR18","unstructured":"Yadav, S., Ramesh, S., Saha, S., Ekbal, A.: Relation extraction from biomedical and clinical text: Unified multitask learning framework (2020)"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Yang, X., Obadinma, S., Zhao, H., Zhang, Q., Matwin, S., Zhu, X.: SemEval-2020 task 5: Counterfactual recognition. In: Proc 14th International Workshop on Semantic Evaluation (SemEval-2020), pp. 322\u2013335. Barcelona, Spain (2020)","DOI":"10.18653\/v1\/2020.semeval-1.40"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Yu, Y., et al.: Low-rank adaptation of large language model rescoring for parameter-efficient speech recognition. In: 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 1\u20138. IEEE (2023)","DOI":"10.1109\/ASRU57964.2023.10389632"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-21477-5_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:18:54Z","timestamp":1779322734000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-21477-5_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032214768","9783032214775"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-21477-5_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Artificial Intelligence Symposium","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Castiglione della Pescaia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2025.icas.events","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}