{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:53:08Z","timestamp":1740102788106,"version":"3.37.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031553257"},{"type":"electronic","value":"9783031553264"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-55326-4_17","type":"book-chapter","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T08:46:10Z","timestamp":1710405970000},"page":"353-369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["UTP: A Unified Term Presentation Tool for Clinical Textual Data Using Pattern-Matching Rules and\u00a0Dictionary-Based Ontologies"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0773-8409","authenticated-orcid":false,"given":"Monah Bou","family":"Hatoum","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0807-4464","authenticated-orcid":false,"given":"Jean Claude","family":"Charr","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1363-6174","authenticated-orcid":false,"given":"Alia","family":"Ghaddar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0195-4378","authenticated-orcid":false,"given":"Christophe","family":"Guyeux","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2580-6660","authenticated-orcid":false,"given":"David","family":"Laiymani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","unstructured":"Abrahamsson, E., Forni, T., Skeppstedt, M., Kvist, M.: Medical text simplification using synonym replacement: adapting assessment of word difficulty to a compounding language (2014). https:\/\/doi.org\/10.3115\/v1\/w14-1207","DOI":"10.3115\/v1\/w14-1207"},{"key":"17_CR2","doi-asserted-by":"publisher","unstructured":"Alsentzer, E., et al.: Publicly available clinical Bert embeddings (2019). https:\/\/doi.org\/10.48550\/ARXIV.1904.03323, https:\/\/arxiv.org\/abs\/1904.03323","DOI":"10.48550\/ARXIV.1904.03323"},{"key":"17_CR3","doi-asserted-by":"publisher","unstructured":"Arora, S., May, A., Zhang, J., R\u00e9, C.: Contextual embeddings: when are they worth it? (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.236, http:\/\/dx.doi.org\/10.18653\/v1\/2020.acl-main.236","DOI":"10.18653\/v1\/2020.acl-main.236"},{"key":"17_CR4","unstructured":"Bird, S., Klein, E., Loper, E.: Natural language processing with Python: analyzing text with the natural language toolkit. \"O\u2019Reilly Media, Inc.\" (2009)"},{"key":"17_CR5","doi-asserted-by":"publisher","unstructured":"Chen, P.F., et al.: Automatic ICD-10 coding and training system: deep neural network based on supervised learning (2021). https:\/\/doi.org\/10.2196\/23230","DOI":"10.2196\/23230"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding (2018). https:\/\/doi.org\/10.48550\/ARXIV.1810.04805, https:\/\/arxiv.org\/abs\/1810.04805","DOI":"10.48550\/ARXIV.1810.04805"},{"key":"17_CR7","doi-asserted-by":"publisher","unstructured":"ElDin, H.G., AbdulRazek, M., Abdelshafi, M., Sahlol, A.T.: Med-flair: medical named entity recognition for diseases and medications based on flair embedding. Proc. Comput. Sci. 189, 67\u201375 (2021). https:\/\/doi.org\/10.1016\/j.procs.2021.05.078, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050921011753. aI in Computational Linguistics","DOI":"10.1016\/j.procs.2021.05.078"},{"key":"17_CR8","doi-asserted-by":"publisher","unstructured":"Hatoum, M., Charr, J.C., Guyeux, C., Laiymani, D., Ghaddar, A.: EMTE: an enhanced medical terms extractor using pattern matching rules (2023). https:\/\/doi.org\/10.5220\/0011717300003393","DOI":"10.5220\/0011717300003393"},{"key":"17_CR9","doi-asserted-by":"publisher","unstructured":"Holper, S., Barmanray, R., Colman, B., Yates, C.J., Liew, D., Smallwood, D.: Ambiguous medical abbreviation study: challenges and opportunities (2020). https:\/\/doi.org\/10.1111\/imj.14442, http:\/\/dx.doi.org\/10.1111\/imj.14442","DOI":"10.1111\/imj.14442"},{"key":"17_CR10","unstructured":"Honnibal, M., Montani, I., Van Landeghem, S., Boyd, A.: spaCy: industrial-strength natural language processing in python (2020)"},{"key":"17_CR11","doi-asserted-by":"publisher","unstructured":"Leaman, R., Khare, R., Lu, Z.: Challenges in clinical natural language processing for automated disorder normalization (2015). https:\/\/doi.org\/10.1016\/j.jbi.2015.07.010, http:\/\/dx.doi.org\/10.1016\/j.jbi.2015.07.010","DOI":"10.1016\/j.jbi.2015.07.010"},{"key":"17_CR12","doi-asserted-by":"publisher","unstructured":"Lee, J., et al.: Biobert: a pre-trained biomedical language representation model for biomedical text mining (2019). https:\/\/doi.org\/10.48550\/ARXIV.1901.08746","DOI":"10.48550\/ARXIV.1901.08746"},{"key":"17_CR13","doi-asserted-by":"publisher","unstructured":"Liu, X., Hersch, G.L., Khalil, I., Devarakonda, M.: Clinical trial information extraction with Bert (2021). https:\/\/doi.org\/10.48550\/ARXIV.2110.10027","DOI":"10.48550\/ARXIV.2110.10027"},{"key":"17_CR14","doi-asserted-by":"publisher","unstructured":"Maciejewski, M.L., Weaver, E.M., Hebert, P.L.: Synonyms in health services research methodology (2010). https:\/\/doi.org\/10.1177\/1077558710372809, http:\/\/dx.doi.org\/10.1177\/1077558710372809","DOI":"10.1177\/1077558710372809"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55\u201360 (2014)","DOI":"10.3115\/v1\/P14-5010"},{"key":"17_CR16","doi-asserted-by":"publisher","unstructured":"Martin, A.K., Green, T.L., McCarthy, A.L., Sowa, P.M., Laakso, E.L.: Healthcare teams: terminology, confusion, and ramifications (2022). https:\/\/doi.org\/10.2147\/jmdh.s342197","DOI":"10.2147\/jmdh.s342197"},{"key":"17_CR17","doi-asserted-by":"publisher","unstructured":"Moons, E., Khanna, A., Akkasi, A., Moens, M.F.: A comparison of deep learning methods for ICD coding of clinical records (2020). https:\/\/doi.org\/10.3390\/app10155262, http:\/\/dx.doi.org\/10.3390\/app10155262","DOI":"10.3390\/app10155262"},{"key":"17_CR18","doi-asserted-by":"publisher","unstructured":"Neumann, M., King, D., Beltagy, I., Ammar, W.: Scispacy: fast and robust models for biomedical natural language processing (2019). https:\/\/doi.org\/10.48550\/ARXIV.1902.07669, https:\/\/arxiv.org\/abs\/1902.07669","DOI":"10.48550\/ARXIV.1902.07669"},{"key":"17_CR19","doi-asserted-by":"publisher","unstructured":"Sammani, A., et al.: Automatic multilabel detection of icd10 codes in Dutch cardiology discharge letters using neural networks (2021). https:\/\/doi.org\/10.1038\/s41746-021-00404-9","DOI":"10.1038\/s41746-021-00404-9"},{"key":"17_CR20","doi-asserted-by":"publisher","unstructured":"Singh, S., Mahmood, A.: The NLP cookbook: Modern recipes for transformer based deep learning architectures (2021). https:\/\/doi.org\/10.1109\/access.2021.3077350, http:\/\/dx.doi.org\/10.1109\/ACCESS.2021.3077350","DOI":"10.1109\/access.2021.3077350"},{"key":"17_CR21","doi-asserted-by":"publisher","unstructured":"Vermeir, P., et al.: Communication in healthcare: a narrative review of the literature and practical recommendations (2015). https:\/\/doi.org\/10.1111\/ijcp.12686, http:\/\/dx.doi.org\/10.1111\/ijcp.12686","DOI":"10.1111\/ijcp.12686"}],"container-title":["Lecture Notes in Computer Science","Agents and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-55326-4_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T08:48:47Z","timestamp":1710406127000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-55326-4_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031553257","9783031553264"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-55326-4_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"15 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Agents and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 February 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 February 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaart2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icaart.scitevents.org\/?y=2023","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":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"306","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":"23","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":"111","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":"8% - 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","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":"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)"}}]}}