{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:16:13Z","timestamp":1743070573863,"version":"3.40.3"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304928"},{"type":"electronic","value":"9783030304935"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30493-5_26","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"243-257","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Text Prior: Weakly Supervised Learning for Assertion Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6670-6909","authenticated-orcid":false,"given":"Vadim","family":"Liventsev","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5959-5382","authenticated-orcid":false,"given":"Irina","family":"Fedulova","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2251-3221","authenticated-orcid":false,"given":"Dmitry","family":"Dylov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"issue":"1","key":"26_CR1","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1197\/jamia.M2950","volume":"16","author":"\u00d6 Uzuner","year":"2009","unstructured":"Uzuner, \u00d6., Zhang, X., Sibanda, T.: Machine learning and rule-based approaches to assertion classification. J. Am. Med. Inform. Assoc. 16(1), 109\u2013115 (2009)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"2","key":"26_CR2","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s10278-017-0030-2","volume":"31","author":"DJ Goff","year":"2018","unstructured":"Goff, D.J., Loehfelm, T.W.: Automated radiology report summarization using an open-source natural language processing pipeline. J. Digit. Imaging 31(2), 185\u2013192 (2018)","journal-title":"J. Digit. Imaging"},{"issue":"suppl-1","key":"26_CR3","doi-asserted-by":"publisher","first-page":"D267","DOI":"10.1093\/nar\/gkh061","volume":"32","author":"O Bodenreider","year":"2004","unstructured":"Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(suppl-1), D267\u2013D270 (2004)","journal-title":"Nucleic Acids Res."},{"issue":"5","key":"26_CR4","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1136\/jamia.2009.001560","volume":"17","author":"CG Chute","year":"2010","unstructured":"Chute, C.G., et al.: Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 17(5), 507\u2013513 (2010). https:\/\/doi.org\/10.1136\/jamia.2009.001560","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"26_CR5","unstructured":"Soldaini, L., Goharian, N.: Quickumls: a fast, unsupervised approach for medical concept extraction. In: MedIR Workshop, sigir (2016)"},{"key":"26_CR6","unstructured":"Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proceedings of the AMIA Symposium, p. 17. American Medical Informatics Association (2001)"},{"issue":"5","key":"26_CR7","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1136\/amiajnl-2011-000203","volume":"18","author":"\u00d6 Uzuner","year":"2011","unstructured":"Uzuner, \u00d6., South, B.R., Shen, S., DuVall, S.L.: 2010 i2b2\/va challenge on concepts, assertions, and relations in clinical text. J. Am. Med. Inform. Assoc. 18(5), 552\u2013556 (2011)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"26_CR8","doi-asserted-by":"publisher","unstructured":"Miranda, E., Aryuni, M., Irwansyah, E.: A survey of medical image classification techniques. In: 2016 International Conference on Information Management and Technology (ICIMTech), pp. 56\u201361, November 2016.https:\/\/doi.org\/10.1109\/ICIMTech.2016.7930302","DOI":"10.1109\/ICIMTech.2016.7930302"},{"key":"26_CR9","unstructured":"Lai, M.: Deep learning for medical image segmentation. arXiv preprint arXiv:1505.02000 (2015)"},{"key":"26_CR10","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: MIMIC-CXR: a large publicly available database of labeled chest radiographs. arXiv preprint arXiv:1901.07042 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. arXiv preprint arXiv:1901.07031 (2019)","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"26_CR13","unstructured":"Rubin, J., Sanghavi, D., Zhao, C., Lee, K., Qadir, A., Xu-Wilson, M.: Large scale automated reading of frontal and lateral chest x-rays using dual convolutional neural networks. arXiv preprint arXiv:1804.07839 (2018)"},{"issue":"5","key":"26_CR14","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1006\/jbin.2001.1029","volume":"34","author":"WW Chapman","year":"2001","unstructured":"Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., Buchanan, B.G.: A simple algorithm for identifying negated findings and diseases in discharge summaries. J. Biomed. Inform. 34(5), 301\u2013310 (2001)","journal-title":"J. Biomed. Inform."},{"key":"26_CR15","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.jbi.2015.02.010","volume":"54","author":"S Mehrabi","year":"2015","unstructured":"Mehrabi, S., et al.: DEEPEN: a negation detection system for clinical text incorporating dependency relation into NegEx. J. Biomed. Inform. 54, 213\u2013219 (2015)","journal-title":"J. Biomed. Inform."},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Enger, M., Velldal, E., \u00d8vrelid, L.: An open-source tool for negation detection: a maximum-margin approach. In: Proceedings of the Workshop Computational Semantics Beyond Events and Roles, pp. 64\u201369 (2017)","DOI":"10.18653\/v1\/W17-1810"},{"key":"26_CR17","unstructured":"Peng, Y., Wang, X., Lu, L., Bagheri, M., Summers, R.M., Lu, Z.: NegBio: a high-performance tool for negation and uncertainty detection in radiology reports. CoRR abs\/1712.05898 (2017). http:\/\/arxiv.org\/abs\/1712.05898"},{"key":"26_CR18","unstructured":"Shelmanov, A., Smirnov, I., Vishneva, E.: Information extraction from clinical texts in Russian. In: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference \u201cDialogue\u201d, vol. 14, pp. 537\u2013549 (2015)"},{"issue":"1","key":"26_CR19","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1186\/s12859-014-0373-3","volume":"15","author":"Z Afzal","year":"2014","unstructured":"Afzal, Z., Pons, E., Kang, N., Sturkenboom, M.C., Schuemie, M.J., Kors, J.A.: ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus. BMC Bioinform. 15(1), 373 (2014)","journal-title":"BMC Bioinform."},{"key":"26_CR20","unstructured":"Sleator, D.D., Temperley, D.: Parsing English with a link grammar. arXiv preprint cmp-lg\/9508004 (1995)"},{"key":"26_CR21","unstructured":"McCray, A.T., Srinivasan, S., Browne, A.C.: Lexical methods for managing variation in biomedical terminologies. In: Proceedings of the Annual Symposium on Computer Application in Medical Care, p. 235. American Medical Informatics Association (1994)"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Chen, D., Manning, C.: A fast and accurate dependency parser using neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 740\u2013750 (2014)","DOI":"10.3115\/v1\/D14-1082"},{"issue":"11","key":"26_CR23","doi-asserted-by":"publisher","first-page":"e112774","DOI":"10.1371\/journal.pone.0112774","volume":"9","author":"S Wu","year":"2014","unstructured":"Wu, S., et al.: Negation\u2019s not solved: generalizability versus optimizability in clinical natural language processing. PLoS One 9(11), e112774 (2014)","journal-title":"PLoS One"},{"key":"26_CR24","unstructured":"Apostolova, E., Tomuro, N., Demner-Fushman, D.: Automatic extraction of lexico-syntactic patterns for detection of negation and speculation scopes. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-Volume 2, pp. 283\u2013287. Association for Computational Linguistics (2011)"},{"key":"26_CR25","unstructured":"Zou, B., Zhou, G., Zhu, Q.: Tree kernel-based negation and speculation scope detection with structured syntactic parse features. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 968\u2013976 (2013)"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Torralba, A., Efros, A.A.: Unbiased look at dataset bias (2011)","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"26_CR27","unstructured":"de Bruijn, B., Cherry, C., Kiritchenko, S., Martin, J., Zhu, X.: NRC at i2b2: one challenge, three practical tasks, nine statistical systems, hundreds of clinical records, millions of useful features"},{"key":"26_CR28","unstructured":"Clark, C., et al.: Determining assertion status for medical problems in clinical records"},{"key":"26_CR29","unstructured":"Demner-Fushman, D., Apostolova, E., Islamaj Dogan, R., et al.: NLM\u2019s system description for the fourth i2b2\/va challenge. In: Proceedings of the 2010 i2b2\/VA Workshop on Challenges in Natural Language Processing for Clinical Data, Boston, MA, USA: i2b2 (2010)"},{"issue":"3","key":"26_CR30","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"issue":"1","key":"26_CR31","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1093\/nsr\/nwx106","volume":"5","author":"ZH Zhou","year":"2017","unstructured":"Zhou, Z.H.: A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5(1), 44\u201353 (2017)","journal-title":"Natl. Sci. Rev."},{"key":"26_CR32","series-title":"Adaptive Computation and Machine Learning Series","volume-title":"Semi-Supervised Learning","author":"BS Olivier Chapelle","year":"2010","unstructured":"Olivier Chapelle, B.S., Zien, A.: Semi-Supervised Learning. Adaptive Computation and Machine Learning Series. MIT Press, Cambridge (2010)"},{"issue":"8","key":"26_CR33","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"26_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00429ED1V01Y201207AIM018","volume":"6","author":"B Settles","year":"2012","unstructured":"Settles, B.: Active learning. Synth. Lect. Artif. Intell. Mach. Learn. 6(1), 1\u2013114 (2012)","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"issue":"2\u20133","key":"26_CR35","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1561\/2200000037","volume":"7","author":"S Hanneke","year":"2014","unstructured":"Hanneke, S., et al.: Theory of disagreement-based active learning. Found. Trends\u00ae Mach. Learn. 7(2\u20133), 131\u2013309 (2014)","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"26_CR36","unstructured":"Zhang, C., Chaudhuri, K.: Beyond disagreement-based agnostic active learning. In: Advances in Neural Information Processing Systems, pp. 442\u2013450 (2014)"},{"key":"26_CR37","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: learning data-driven curriculum for very deep neural networks on corrupted labels. arXiv preprint arXiv:1712.05055 (2017)"},{"key":"26_CR38","unstructured":"Kumar, M.P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: Advances in Neural Information Processing Systems, pp. 1189\u20131197 (2010)"},{"key":"26_CR39","doi-asserted-by":"crossref","unstructured":"Jiang, L., Meng, D., Zhao, Q., Shan, S., Hauptmann, A.G.: Self-paced curriculum learning. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9608"},{"key":"26_CR40","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. CoRR abs\/1607.04606 (2016). http:\/\/arxiv.org\/abs\/1607.04606"},{"key":"26_CR41","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)"},{"key":"26_CR42","doi-asserted-by":"crossref","unstructured":"Chelba, C., et al.: One billion word benchmark for measuring progress in statistical language modeling. arXiv preprint arXiv:1312.3005 (2013)","DOI":"10.21437\/Interspeech.2014-564"},{"key":"26_CR43","unstructured":"Vaswani, A., et al.: Attention is all you need. CoRR abs\/1706.03762 (2017). http:\/\/arxiv.org\/abs\/1706.03762"},{"issue":"8","key":"26_CR44","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"26_CR45","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs\/1412.6980 (2014). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"26_CR46","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256 (2010)"},{"issue":"1","key":"26_CR47","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/1471-2105-7-91","volume":"7","author":"S Varma","year":"2006","unstructured":"Varma, S., Simon, R.: Bias in error estimation when using cross-validation for model selection. BMC Bioinform. 7(1), 91 (2006)","journal-title":"BMC Bioinform."},{"issue":"1","key":"26_CR48","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/j.0039-3193.2004.00109.x","volume":"58","author":"B Sigurd","year":"2004","unstructured":"Sigurd, B., Eeg-Olofsson, M., Van De Weijer, J.: Word length, sentence length and frequency - Zipf revisited. Studia Linguistica 58(1), 37\u201352 (2004). https:\/\/doi.org\/10.1111\/j.0039-3193.2004.00109.x","journal-title":"Studia Linguistica"},{"key":"26_CR49","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (2018)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30493-5_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:50:09Z","timestamp":1710348609000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30493-5_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304928","9783030304935"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30493-5_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}