{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:07:43Z","timestamp":1774915663649,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031924736","type":"print"},{"value":"9783031924743","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-92474-3_26","type":"book-chapter","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T18:15:13Z","timestamp":1747332913000},"page":"435-451","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Zero-Shot Approaches for\u00a0the\u00a0Extraction of\u00a0Event Logs from\u00a0Medical Notes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3926-6653","authenticated-orcid":false,"given":"Allmin","family":"Susaiyah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9223-938X","authenticated-orcid":false,"given":"Natalia","family":"Sidorova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"issue":"1","key":"26_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2013\/507984","volume":"2013","author":"W van der Aalst","year":"2013","unstructured":"van der Aalst, W.: Business process management: a comprehensive survey. ISRN Softw. Eng. 2013(1), 1\u201337 (2013)","journal-title":"ISRN Softw. Eng."},{"key":"26_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49851-4","volume-title":"Process Mining - Data Science in Action","author":"W van der Aalst","year":"2016","unstructured":"van der Aalst, W.: Process Mining - Data Science in Action, 2nd edn. Springer, New York (2016)","edition":"2"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"van Aken, B., Trajanovska, I., Siu, A., Mayrdorfer, M., Budde, K., Loeser, A.: Assertion detection in clinical notes: Medical language models to the rescue? In: Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations. Association for Computational Linguistics, Online (2021)","DOI":"10.18653\/v1\/2021.nlpmc-1.5"},{"key":"26_CR4","unstructured":"Bond, F., Foster, R.: Linking and extending an open multilingual wordnet. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1352\u20131362 (2013)"},{"key":"26_CR5","unstructured":"Bundy, H., et al.: Can the administrative loads of physicians be alleviated by AI-facilitated clinical documentation? J. Gen. Intern. Med.\u00a01\u20136 (2024)"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Chen, R., Qin, C., Jiang, W., Choi, D.: Is a large language model a good annotator for event extraction? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 16, pp. 17772\u201317780 (2024)","DOI":"10.1609\/aaai.v38i16.29730"},{"key":"26_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (2019)"},{"issue":"3","key":"26_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1346","volume":"10","author":"K Diba","year":"2020","unstructured":"Diba, K., Batoulis, K., Weidlich, M., Weske, M.: Extraction, correlation, and abstraction of event data for process mining. Wiley Interdisc. Rev. Data Min. Knowl. Discovery 10(3), e1346 (2020)","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Discovery"},{"key":"26_CR9","unstructured":"van Dijk, T.: Using Natural Language Processing for Event Classification in Dutch Medical Texts. Master\u2019s thesis, Eindhoven University of Technology (2024)"},{"key":"26_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-56509-4","volume-title":"Fundamentals of Business Process Management","author":"M Dumas","year":"2018","unstructured":"Dumas, M., Rosa, L.M., Mendling, J., Reijers, A.H.: Fundamentals of Business Process Management. Springer, Heidelberg (2018)"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Elbattah, M., Arnaud, \u00c9., Gignon, M., Dequen, G.: The role of text analytics in healthcare: a review of recent developments and applications. Healthinf, 825\u2013832 (2021)","DOI":"10.5220\/0010414508250832"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Gaudet-Blavignac, C., Foufi, V., Bjelogrlic, M., Lovis, C.: Use of the systematized nomenclature of medicine clinical terms (SNOMED CT) for processing free text in health care: systematic scoping review. J. Med. Internet Res. 23(1) (2021)","DOI":"10.2196\/24594"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Geeganage, D.T.K., Wynn, M.T., ter Hofstede, A.H.: Text2el: exploiting unstructured text for event log enrichment. In: 2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp.\u00a01\u20138. IEEE (2022)","DOI":"10.1109\/SITIS57111.2022.00010"},{"issue":"1","key":"26_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3640018","volume":"16","author":"D Geeganage","year":"2024","unstructured":"Geeganage, D., Wynn, M.T., ter Hofstede, A.H.: Text2el+: Expert guided event log enrichment using unstructured text. ACM J. Data Inf. Qual. 16(1), 1\u201328 (2024)","journal-title":"ACM J. Data Inf. Qual."},{"issue":"12","key":"26_CR15","doi-asserted-by":"publisher","first-page":"2629","DOI":"10.1007\/s10439-023-03272-4","volume":"51","author":"L Giray","year":"2023","unstructured":"Giray, L.: Prompt engineering with chatgpt: a guide for academic writers. Ann. Biomed. Eng. 51(12), 2629\u20132633 (2023)","journal-title":"Ann. Biomed. Eng."},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Huang, F., et al.: A three-stage framework for event-event relation extraction with large language model. In: International Conference on Neural Information Processing, pp. 434\u2013446 (2023)","DOI":"10.1007\/978-981-99-8181-6_33"},{"key":"26_CR17","unstructured":"Johnson, A., Pollard, T., Mark, R.: MIMIC-III clinical database (version 1.4). PhysioNet 10(C2XW26), 2 (2016)"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Koenecke, A., Choi, A.S.G., Mei, K.X., Schellmann, H., Sloane, M.: Careless whisper: speech-to-text hallucination harms. In: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 1672\u20131681. Association for Computing Machinery, New York (2024)","DOI":"10.1145\/3630106.3658996"},{"key":"26_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2019.104053","volume":"135","author":"ZT Korach","year":"2020","unstructured":"Korach, Z.T., et al.: Mining clinical phrases from nursing notes to discover risk factors of patient deterioration. Int. J. Med. Inform. 135, 104053 (2020)","journal-title":"Int. J. Med. Inform."},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Kurniati, A.P., Hall, G., Hogg, D., Johnson, O.: Process mining in oncology using the MIMIC-III dataset. In: International Conference on Data and Information Science (ICoDIS) 2017, vol.\u00a0971, p. 012008. IOP Publishing (2018)","DOI":"10.1088\/1742-6596\/971\/1\/012008"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Li, F., et al.: Event extraction as multi-turn question answering. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 829\u2013838 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.73"},{"key":"26_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2022.100511","volume":"46","author":"I Li","year":"2022","unstructured":"Li, I., et al.: Neural natural language processing for unstructured data in electronic health records: a review. Comput. Sci. Rev. 46, 100511 (2022)","journal-title":"Comput. Sci. Rev."},{"key":"26_CR23","doi-asserted-by":"publisher","first-page":"6301","DOI":"10.1109\/TNNLS.2022.3213168","volume":"35","author":"Q Li","year":"2021","unstructured":"Li, Q., Li, J., Sheng, J., Cui, S., Wu, J., Hei, Y., Peng, H., Guo, S., Wang, L., Beheshti, A., et al.: A survey on deep learning event extraction: approaches and applications. IEEE Trans. Neural Netw. Learn. Syst. 35, 6301\u20136321 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"26_CR24","unstructured":"Mohan, S., Li, D.: Medmentions: a large biomedical corpus annotated with UMLS concepts. In: Automated Knowledge Base Construction (AKBC) (2019)"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Ning, Q., Feng, Z., Roth, D.: A structured learning approach to temporal relation extraction. In: Palmer, M., Hwa, R., Riedel, S. (eds.) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1027\u20131037. Association for Computational Linguistics, Copenhagen (2017)","DOI":"10.18653\/v1\/D17-1108"},{"issue":"9","key":"26_CR26","doi-asserted-by":"publisher","first-page":"1844","DOI":"10.1093\/jamia\/ocae029","volume":"31","author":"F Remy","year":"2024","unstructured":"Remy, F., Demuynck, K., Demeester, T.: BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights. J. Am. Med. Inform. Assoc. 31(9), 1844\u20131855 (2024)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"26_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/978-3-030-58666-9_29","volume-title":"Business Process Management","author":"S Remy","year":"2020","unstructured":"Remy, S., Pufahl, L., Sachs, J.P., B\u00f6ttinger, E., Weske, M.: Event log generation in a health system: a case study. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 505\u2013522. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58666-9_29"},{"key":"26_CR28","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/s10791-008-9061-0","volume":"11","author":"L Rokach","year":"2008","unstructured":"Rokach, L., Romano, R., Maimon, O.: Negation recognition in medical narrative reports. Inf. Retrieval 11, 499\u2013538 (2008)","journal-title":"Inf. Retrieval"},{"key":"26_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110675","volume":"275","author":"S Scaboro","year":"2023","unstructured":"Scaboro, S., Portelli, B., Chersoni, E., Santus, E., Serra, G.: Extensive evaluation of transformer-based architectures for adverse drug events extraction. Knowl.-Based Syst. 275, 110675 (2023)","journal-title":"Knowl.-Based Syst."},{"key":"26_CR30","unstructured":"Touvron, H., et\u00a0al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"key":"26_CR31","unstructured":"Walker, C., Strassel, S., Medero, J., Maeda, K.: ACE 2005 multilingual training corpus (2006)"},{"key":"26_CR32","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: MAVEN: a massive general domain event detection dataset. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1652\u20131671. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.129"},{"key":"26_CR33","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Jin, X., Wang, Y., Cheng, X.: Document embedding enhanced event detection with hierarchical and supervised attention. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 414\u2013419 (2018)","DOI":"10.18653\/v1\/P18-2066"}],"container-title":["Lecture Notes in Business Information Processing","Research Challenges in Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-92474-3_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T18:15:31Z","timestamp":1747332931000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-92474-3_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031924736","9783031924743"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-92474-3_26","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"16 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RCIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Research Challenges in Information Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seville","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"20 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rcis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.rcis-conf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}