{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T11:54:53Z","timestamp":1767182093381,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811623769"},{"type":"electronic","value":"9789811623776"}],"license":[{"start":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T00:00:00Z","timestamp":1632441600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T00:00:00Z","timestamp":1632441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-2377-6_50","type":"book-chapter","created":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T01:44:09Z","timestamp":1632447849000},"page":"533-542","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Clinical Note Section Identification Using Transfer Learning"],"prefix":"10.1007","author":[{"given":"Namrata","family":"Nair","sequence":"first","affiliation":[]},{"given":"Sankaran","family":"Narayanan","sequence":"additional","affiliation":[]},{"given":"Pradeep","family":"Achan","sequence":"additional","affiliation":[]},{"given":"K. P.","family":"Soman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,24]]},"reference":[{"key":"50_CR1","doi-asserted-by":"crossref","unstructured":"Pomares-Quimbaya A, Kreuzthaler M, Schulz S (2019) Current approaches to identify sections within clinical narratives from electronic health records: a systematic review. BMC Med Res Methodol 19(1):155","DOI":"10.1186\/s12874-019-0792-y"},{"key":"50_CR2","unstructured":"Podder V, Lew V, Ghassemzadeh S (2020) SOAP Notes. In: StatPearls [Internet]. StatPearls Publishing. https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK482263\/"},{"issue":"5","key":"50_CR3","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.ijmedinf.2007.09.001","volume":"77","author":"Kristiina Hayrinen","year":"2008","unstructured":"Hayrinen Kristiina, Saranto Kaija, Nykanen Pirkko (2008) Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform 77(5):291\u2013304","journal-title":"Int J Med Inform"},{"key":"50_CR4","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.jbi.2017.11.011","volume":"77","author":"Yanshan Wang","year":"2018","unstructured":"Wang Yanshan et al (2018) Clinical information extraction applications: a literature review. J Biomed Inform 77:34\u201349","journal-title":"J Biomed Inform"},{"key":"50_CR5","unstructured":"Denny JC et al (2008) Development and evaluation of a clinical note section header terminology. In: AMIA annual symposium proceedings, vol 2008. American Medical Informatics Association, 156p"},{"key":"50_CR6","doi-asserted-by":"crossref","unstructured":"Sadoughi N et al (2018) Detecting section boundaries in medical dictations: Toward real-time conversion of medical dictations to clinical reports. In: International conference on speech and computer. Springer, pp 563\u2013573","DOI":"10.1007\/978-3-319-99579-3_58"},{"key":"50_CR7","doi-asserted-by":"crossref","unstructured":"Jeblee S et al (2019) Extracting relevant information from physician-patient dialogues for automated clinical note taking. In: Proceedings of the tenth international workshop on health text mining and information analysis (LOUHI 2019), pp 65\u201374","DOI":"10.18653\/v1\/D19-6209"},{"key":"50_CR8","doi-asserted-by":"crossref","unstructured":"Jonnalagadda SR et al (2017) Text mining of the electronic health record: an information extraction approach for automated identification and subphenotyping of HFPEF patients for clinical trials. J Cardiovasc Transl Res 10(3):313\u2013321","DOI":"10.1007\/s12265-017-9752-2"},{"issue":"5","key":"50_CR9","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1093\/jamia\/ocx160","volume":"25","author":"Wu Honghan","year":"2018","unstructured":"Honghan Wu et al (2018) SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research. J Am Med Inform Assoc 25(5):530\u2013537","journal-title":"J Am Med Inform Assoc"},{"key":"50_CR10","doi-asserted-by":"crossref","unstructured":"Savova GK et al (2010) Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 17(5):507\u2013513","DOI":"10.1136\/jamia.2009.001560"},{"issue":"3","key":"50_CR11","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1093\/jamia\/ocx132","volume":"25","author":"Ergin Soysal","year":"2018","unstructured":"Soysal Ergin et al (2018) CLAMP-a toolkit for efficiently building customized clinical natural language processing pipelines. J Am Med Inform Assoc 25(3):331\u2013336","journal-title":"J Am Med Inform Assoc"},{"key":"50_CR12","unstructured":"Georgia Tech Research Institute (2018) ClarityNLP Section Tagging. https:\/\/claritynlp.readthedocs.io\/en\/latest\/developer guide\/algorithms\/section tagger.html"},{"key":"50_CR13","doi-asserted-by":"crossref","unstructured":"Li Y, Lipsky Gorman S, Elhadad N (2010) Section classification in clinical notes using supervised hidden markov model. In: Proceedings of the 1st ACM international health informatics symposium (2010), pp 744\u2013750","DOI":"10.1145\/1882992.1883105"},{"key":"50_CR14","doi-asserted-by":"crossref","unstructured":"Dai HJ et al (2015) Recognition and evaluation of clinical section headings in clinical documents using token-based formulation with conditional random fields. BioMed Res Int","DOI":"10.1155\/2015\/873012"},{"issue":"1","key":"50_CR15","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.jbi.2011.08.020","volume":"45","author":"Danielle Mowery","year":"2012","unstructured":"Mowery Danielle et al (2012) Building an automated SOAP classifier for emergency department reports. J Biomed Inform 45(1):71\u201381","journal-title":"J Biomed Inform"},{"key":"50_CR16","first-page":"35","volume":"216","author":"Jian Ni","year":"2015","unstructured":"Ni Jian, Delaney Brian, Florian Radu (2015) Fast model adaptation for automated section classification in electronic medical records. Stud Health Technol Inform 216:35\u201339","journal-title":"Stud Health Technol Inform"},{"key":"50_CR17","doi-asserted-by":"crossref","unstructured":"Barathi Ganesh HB et al (2020) MedNLU: natural language understander for medical texts. In: Deep learning techniques for biomedical and health informatics. Springer, pp 3\u201321","DOI":"10.1007\/978-3-030-33966-1_1"},{"key":"50_CR18","doi-asserted-by":"crossref","unstructured":"Beam AL et al (2018) Clinical concept embeddings learned from massive sources of multimodal medical data arXiv:1804.01486","DOI":"10.1142\/9789811215636_0027"},{"key":"50_CR19","doi-asserted-by":"crossref","unstructured":"Yu M, Dredze M (2014) Improving lexical embeddings with semantic knowledge. In: Proceedings of the 52nd annual meeting of the association for computational linguistics, vol 2: Short Papers, pp 545\u2013550","DOI":"10.3115\/v1\/P14-2089"},{"issue":"suppl 1","key":"50_CR20","doi-asserted-by":"publisher","first-page":"D267","DOI":"10.1093\/nar\/gkh061","volume":"32","author":"Olivier Bodenreider","year":"2004","unstructured":"Bodenreider Olivier (2004) The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 32(suppl 1):D267\u2013D270","journal-title":"Nucleic Acids Res"},{"key":"50_CR21","unstructured":"Devlin J et al (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805"},{"key":"50_CR22","doi-asserted-by":"publisher","unstructured":"Alsentzer E et al (2019) Publicly available clinical BERT embeddings. In: Proceedings of the 2nd clinical natural language processing workshop. Association for Computational Linguistics, Minneapolis, Minnesota, USA, June 2019, pp 72\u201378. https:\/\/doi.org\/10.18653\/v1\/W19-1909https:\/\/www.aclweb.org\/anthology\/W19-1909","DOI":"10.18653\/v1\/W19-1909"},{"key":"50_CR23","doi-asserted-by":"crossref","unstructured":"Peters ME et al (2018) Deep contextualized word representations. In: Proc. of NAACL","DOI":"10.18653\/v1\/N18-1202"},{"key":"50_CR24","unstructured":"Akbik A, Blythe D, Vollgraf R (2018) Contextual string embeddings for sequence labeling. In: Proceedings of the 27th international conference on computational linguistics, pp 1638\u20131649"},{"issue":"5","key":"50_CR25","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1136\/amiajnl-2011-000203","volume":"18","author":"Ozlem Uzuner","year":"2011","unstructured":"Uzuner Ozlem et al (2011) 2010 i2b2\/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 18(5):552\u2013556","journal-title":"J Am Med Inform Assoc"},{"key":"50_CR26","doi-asserted-by":"crossref","unstructured":"Narayanan S et al (2020) Evaluation of transfer learning for Adverse Drug Event (ADE) and medication entity extraction. In: Proceedings of the 3rd clinical natural language processing workshop. Association for Computational Linguistics, pp 55\u201364. https:\/\/www.aclweb.org\/anthology\/2020.clinicalnlp-1.6","DOI":"10.18653\/v1\/2020.clinicalnlp-1.6"},{"key":"50_CR27","unstructured":"Harvard University Medical School (2020) n2c2 NLP Research Datasets https:\/\/portal.dbmi.hms.harvard.edu\/projects\/n2c2-nlp\/"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of Sixth International Congress on Information and Communication Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-2377-6_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T18:18:22Z","timestamp":1644430702000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-2377-6_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,24]]},"ISBN":["9789811623769","9789811623776"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-2377-6_50","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2021,9,24]]},"assertion":[{"value":"24 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}