{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:34:06Z","timestamp":1775147646917,"version":"3.50.1"},"reference-count":88,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T00:00:00Z","timestamp":1674691200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T00:00:00Z","timestamp":1674691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Extracting relevant information about infectious diseases is an essential task. However, a significant obstacle in supporting public health research is the lack of methods for effectively mining large amounts of health data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The named entity recognition implementation in the NLP layer achieves a performance gain of about 1\u20133% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1\u20138% better). A thorough examination reveals the disease\u2019s presence and symptoms prevalence in patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-023-02117-3","type":"journal-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T14:03:58Z","timestamp":1674741838000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach"],"prefix":"10.1186","volume":"23","author":[{"given":"Shaina","family":"Raza","sequence":"first","affiliation":[]},{"given":"Brian","family":"Schwartz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,26]]},"reference":[{"key":"2117_CR1","unstructured":"Ourworldindata.org. COVID-19 Data Explorer. Our world in data. 2022."},{"key":"2117_CR2","doi-asserted-by":"crossref","unstructured":"Flor LS, Friedman J, Spencer CN, Cagney J, Arrieta A, Herbert ME, et al. Quantifying the effects of the COVID-19 pandemic on gender equality on health, social, and economic indicators: a comprehensive review of data from March, 2020, to September, 2021. Lancet. 2022.","DOI":"10.1016\/S0140-6736(22)00008-3"},{"key":"2117_CR3","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1093\/pubmed\/fdaa136","volume":"42","author":"JM Baena-Di\u00e9z","year":"2020","unstructured":"Baena-Di\u00e9z JM, Barroso M, Cordeiro-Coelho SI, Di\u00e1z JL, Grau M. Impact of COVID-19 outbreak by income: hitting hardest the most deprived. J Public Heal. 2020;42:698\u2013703.","journal-title":"J Public Heal"},{"key":"2117_CR4","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.bpa.2020.11.009","volume":"35","author":"AD Kaye","year":"2021","unstructured":"Kaye AD, Okeagu CN, Pham AD, Silva RA, Hurley JJ, Arron BL, et al. Economic impact of COVID-19 pandemic on healthcare facilities and systems: International perspectives. Best Pract Res Clin Anaesthesiol. 2021;35:293\u2013306.","journal-title":"Best Pract Res Clin Anaesthesiol"},{"key":"2117_CR5","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1038\/s41586-020-2521-4","volume":"584","author":"EJ Williamson","year":"2020","unstructured":"Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584:430\u20136.","journal-title":"Nature"},{"key":"2117_CR6","doi-asserted-by":"crossref","unstructured":"Caufield JH, Zhou Y, Bai Y, Liem DA, Garlid AO, Chang K-W, et al. A comprehensive typing system for information extraction from clinical narratives. medRxiv. 2019;19009118.","DOI":"10.1101\/19009118"},{"key":"2117_CR7","unstructured":"Raza S, Schwartz B. Detecting biomedical named entities in COVID-19 texts. In: Workshop on healthcare AI and COVID-19, ICML 2022; 2022."},{"key":"2117_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. J Big Data. 2016;3:1\u201340.","journal-title":"J Big Data"},{"key":"2117_CR9","first-page":"201","volume":"15","author":"B Settles","year":"2010","unstructured":"Settles B. Active learning literature survey. Mach Learn. 2010;15:201\u201321.","journal-title":"Mach Learn"},{"key":"2117_CR10","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1075\/li.30.1.03nad","volume":"30","author":"D Nadeau","year":"2007","unstructured":"Nadeau D, Sekine S. A survey of named entity recognition and classification. Lingvisticae Investig. 2007;30:3\u201326.","journal-title":"Lingvisticae Investig"},{"key":"2117_CR11","first-page":"175","volume":"11","author":"D Campos","year":"2012","unstructured":"Campos D, Matos S, Oliveira JL. Biomedical named entity recognition: a survey of machine-learning tools. Theory Appl Adv Text Min. 2012;11:175\u201395.","journal-title":"Theory Appl Adv Text Min"},{"key":"2117_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3321-4","volume":"20","author":"H Cho","year":"2019","unstructured":"Cho H, Lee H. Biomedical named entity recognition using deep neural networks with contextual information. BMC Bioinform. 2019;20:1\u201311.","journal-title":"BMC Bioinform"},{"key":"2117_CR13","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36:1234\u201340.","journal-title":"Bioinformatics"},{"key":"2117_CR14","doi-asserted-by":"crossref","unstructured":"Alsentzer E, Murphy JR, Boag W, Weng W-H, Jin D, Naumann T, et al. Publicly available clinical BERT embeddings. Preprint http:\/\/arxiv.org\/abs\/190403323. 2019.","DOI":"10.18653\/v1\/W19-1909"},{"key":"2117_CR15","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1186\/s12859-022-04751-6","volume":"23","author":"S Raza","year":"2022","unstructured":"Raza S, Schwartz B, Rosella LC. CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice. BMC Bioinform. 2022;23:210.","journal-title":"BMC Bioinform"},{"key":"2117_CR16","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.compbiomed.2019.04.002","volume":"108","author":"K Xu","year":"2019","unstructured":"Xu K, Yang Z, Kang P, Wang Q, Liu W. Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition. Comput Biol Med. 2019;108:122\u201332.","journal-title":"Comput Biol Med"},{"issue":"2","key":"2117_CR17","doi-asserted-by":"publisher","first-page":"e0246310","DOI":"10.1371\/journal.pone.0246310","volume":"16","author":"S Gao","year":"2021","unstructured":"Gao S, Kotevska O, Sorokine A, Christian JB. A pre-training and self-training approach for biomedical named entity recognition. PLoS ONE. 2021;16(2):e0246310.","journal-title":"PLoS ONE"},{"key":"2117_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103511","volume":"108","author":"C Wu","year":"2020","unstructured":"Wu C, Luo G, Guo C, Ren Y, Zheng A, Yang C. An attention-based multi-task model for named entity recognition and intent analysis of Chinese online medical questions. J Biomed Inform. 2020;108: 103511.","journal-title":"J Biomed Inform"},{"key":"2117_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-017-1776-8","volume":"18","author":"G Crichton","year":"2017","unstructured":"Crichton G, Pyysalo S, Chiu B, Korhonen A. A neural network multi-task learning approach to biomedical named entity recognition. BMC Bioinform. 2017;18:1\u201314.","journal-title":"BMC Bioinform"},{"key":"2117_CR20","doi-asserted-by":"publisher","first-page":"5955","DOI":"10.12998\/wjcc.v9.i21.5955","volume":"9","author":"X Du","year":"2021","unstructured":"Du X, Kang K, Chong Y, Zhang ML, Yang W, Meng XL, et al. COVID-19 patient with an incubation period of 27 d: a case report. World J Clin Cases. 2021;9:5955\u201362.","journal-title":"World J Clin Cases"},{"key":"2117_CR21","unstructured":"Kumar S. A survey of deep learning methods for relation extraction. Preprint http:\/\/arxiv.org\/abs\/170503645. 2017."},{"key":"2117_CR22","doi-asserted-by":"crossref","unstructured":"Zhou D, Zhong D, He Y. Biomedical relation extraction: from binary to complex. Comput Math Methods Med. 2014;2014.","DOI":"10.1155\/2014\/298473"},{"key":"2117_CR23","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1007\/s10115-022-01665-w","volume":"64","author":"J Yang","year":"2022","unstructured":"Yang J, Han SC, Poon J. A survey on extraction of causal relations from natural language text. Knowl Inf Syst. 2022;64:1161\u201386.","journal-title":"Knowl Inf Syst"},{"key":"2117_CR24","unstructured":"Zeng D, Liu K, Lai S, Zhou G, Zhao J. Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, 2014. p. 2335\u201344."},{"key":"2117_CR25","doi-asserted-by":"crossref","unstructured":"Miwa M, Bansal M. End-to-end relation extraction using lstms on sequences and tree structures. Preprint http:\/\/arxiv.org\/abs\/160100770. 2016.","DOI":"10.18653\/v1\/P16-1105"},{"key":"2117_CR26","unstructured":"Pushp PK, Srivastava MM. Train once, test anywhere: zero-shot learning for text classification. Preprint http:\/\/arxiv.org\/abs\/171205972. 2017."},{"key":"2117_CR27","doi-asserted-by":"crossref","unstructured":"Levy O, Seo M, Choi E, Zettlemoyer L. Zero-shot relation extraction via reading comprehension. Preprint http:\/\/arxiv.org\/abs\/170604115. 2017.","DOI":"10.18653\/v1\/K17-1034"},{"key":"2117_CR28","doi-asserted-by":"crossref","unstructured":"Obamuyide A, Vlachos A. Zero-shot relation classification as textual entailment. In: Proceedings of the first workshop on fact extraction and VERification (FEVER). 2018. p. 72\u20138.","DOI":"10.18653\/v1\/W18-5511"},{"key":"2117_CR29","doi-asserted-by":"crossref","unstructured":"Chen C-Y, Li C-T. ZS-BERT: Towards zero-shot relation extraction with attribute representation learning. In: Toutanova K, Rumshisky A, Zettlemoyer L, Hakkani-T\u00fcr D, Beltagy I, Bethard S, et al., editors. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, {NAACL-HLT} 2021, Online, June 6\u201311, 2021. Association for Computational Linguistics; 2021. p. 3470\u20139.","DOI":"10.18653\/v1\/2021.naacl-main.272"},{"key":"2117_CR30","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. Preprint http:\/\/arxiv.org\/abs\/181004805. 2018."},{"key":"2117_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103761","volume":"117","author":"K Lybarger","year":"2021","unstructured":"Lybarger K, Ostendorf M, Thompson M, Yetisgen M. Extracting COVID-19 diagnoses and symptoms from clinical text: a new annotated corpus and neural event extraction framework. J Biomed Inform. 2021;117: 103761.","journal-title":"J Biomed Inform"},{"key":"2117_CR32","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1109\/JBHI.2021.3123192","volume":"26","author":"X Luo","year":"2021","unstructured":"Luo X, Gandhi P, Storey S, Huang K. A deep language model for symptom extraction from clinical text and its application to extract covid-19 symptoms from social media. IEEE J Biomed Heal Inform. 2021;26:1737\u201348.","journal-title":"IEEE J Biomed Heal Inform"},{"key":"2117_CR33","unstructured":"Lu Wang L, Lo K, Chandrasekhar Y, Reas R, Yang J, Eide D, et al. CORD-19: the Covid-19 open research dataset. 2020."},{"key":"2117_CR34","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1613\/jair.1.12631","volume":"72","author":"GM Silverman","year":"2021","unstructured":"Silverman GM, Sahoo HS, Ingraham NE, Lupei M, Puskarich MA, Usher M, et al. Nlp methods for extraction of symptoms from unstructured data for use in prognostic covid-19 analytic models. J Artif Intell Res. 2021;72:429\u201374.","journal-title":"J Artif Intell Res"},{"key":"2117_CR35","doi-asserted-by":"crossref","unstructured":"Girju R. Automatic detection of causal relations for question answering. 2003;76\u201383.","DOI":"10.3115\/1119312.1119322"},{"key":"2117_CR36","unstructured":"Hsieh Y-L, Chang Y-C, Chang N-W, Hsu W-L. Identifying protein-protein interactions in biomedical literature using recurrent neural networks with long short-term memory. In: Proceedings of the eighth international joint conference on natural language processing (volume 2: short papers). 2017. pp. 240\u20135."},{"key":"2117_CR37","doi-asserted-by":"crossref","unstructured":"Zhao S, Hu M, Cai Z, Liu F. Modeling dense cross-modal interactions for joint entity-relation extraction. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. 2021. pp. 4032\u20138.","DOI":"10.24963\/ijcai.2020\/558"},{"key":"2117_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103451","volume":"106","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Li L, Lu H, Zhou A, Qin X. Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions. J Biomed Inform. 2020;106: 103451.","journal-title":"J Biomed Inform"},{"key":"2117_CR39","unstructured":"Lung J. Application of NLP to extract biomedical entities from COVID-19 papers. 2021."},{"key":"2117_CR40","doi-asserted-by":"publisher","unstructured":"Liu Z, Yang M, Wang X, Chen Q, Tang B, Wang Z, et al. Entity recognition from clinical texts via recurrent neural network. https:\/\/doi.org\/10.1186\/s12911-017-0468-7.","DOI":"10.1186\/s12911-017-0468-7"},{"key":"2117_CR41","unstructured":"Zhou Y, Ju C, Caufield JH, Shih K, Chen C, Sun Y, et al. Clinical named entity recognition using contextualized token representations. 2021."},{"key":"2117_CR42","doi-asserted-by":"publisher","first-page":"673","DOI":"10.3389\/fcell.2020.00673","volume":"8","author":"N Perera","year":"2020","unstructured":"Perera N, Dehmer M, Emmert-Streib F. Named entity recognition and relation detection for biomedical information extraction. Front Cell Dev Biol. 2020;8:673.","journal-title":"Front Cell Dev Biol"},{"key":"2117_CR43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13256-016-1164-4","volume":"11","author":"RA Rison","year":"2017","unstructured":"Rison RA, Shepphird JK, Kidd MR. How to choose the best journal for your case report. J Med Case Rep. 2017;11:1\u20139.","journal-title":"J Med Case Rep"},{"key":"2117_CR44","unstructured":"National Center for Biotechnology Information. Definitions. 2020. https:\/\/www.ncbi.nlm.nih.gov."},{"key":"2117_CR45","unstructured":"IMI. CARE case report guidelines. 2019."},{"key":"2117_CR46","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.jclinepi.2019.10.011","volume":"118","author":"B Nussbaumer-Streit","year":"2020","unstructured":"Nussbaumer-Streit B, Klerings I, Dobrescu AI, Persad E, Stevens A, Garritty C, et al. Excluding non-English publications from evidence-syntheses did not change conclusions: a meta-epidemiological study. J Clin Epidemiol. 2020;118:42\u201354.","journal-title":"J Clin Epidemiol"},{"key":"2117_CR47","unstructured":"Spark OCR- John Snow Labs. 2022. https:\/\/nlp.johnsnowlabs.com\/docs\/en\/ocr."},{"key":"2117_CR48","unstructured":"Elasticsearch. 2014. https:\/\/www.elastic.co."},{"key":"2117_CR49","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1126\/science.156.3776.754","volume":"156","author":"EL Brady","year":"1967","unstructured":"Brady EL, Wallenstein MB. The national standard reference data system. Science. 1967;156:754\u201362.","journal-title":"Science"},{"key":"2117_CR50","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1590\/2176-9451.19.5.027-030.ebo","volume":"19","author":"JR Cardoso","year":"2014","unstructured":"Cardoso JR, Pereira LM, Iversen MD, Ramos AL. What is gold standard and what is ground truth? Dent Press J Orthod. 2014;19:27\u201330.","journal-title":"Dent Press J Orthod"},{"key":"2117_CR51","unstructured":"Caufield JH. MACCROBAT. 2020. 10.6084\/m9.figshare.9764942.v2."},{"key":"2117_CR52","unstructured":"Annotation Lab - FREE by John Snow Labs. 2022."},{"key":"2117_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jbi.2013.12.006","volume":"47","author":"RI Do\u011fan","year":"2014","unstructured":"Do\u011fan RI, Leaman R, Lu Z. NCBI disease corpus: a resource for disease name recognition and concept normalization. J Biomed Inform. 2014;47:1\u201310.","journal-title":"J Biomed Inform"},{"key":"2117_CR54","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.artint.2012.03.006","volume":"194","author":"J Nothman","year":"2013","unstructured":"Nothman J, Ringland N, Radford W, Murphy T, Curran JR. Learning multilingual named entity recognition from Wikipedia. Artif Intell. 2013;194:151\u201375.","journal-title":"Artif Intell"},{"key":"2117_CR55","doi-asserted-by":"crossref","unstructured":"Artstein R. Inter-annotator agreement. In: Handbook of linguistic annotation. Springer; 2017. p. 297\u2013313.","DOI":"10.1007\/978-94-024-0881-2_11"},{"key":"2117_CR56","doi-asserted-by":"crossref","unstructured":"Tjong Kim Sang EF, de Meulder F. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: Proc 7th Conf Nat Lang Learn CoNLL 2003 HLT-NAACL 2003; 2003. pp. 142\u20137.","DOI":"10.3115\/1119176.1119195"},{"key":"2117_CR57","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.jbi.2015.09.010","volume":"58","author":"Y Chen","year":"2015","unstructured":"Chen Y, Lasko TA, Mei Q, Denny JC, Xu H. A study of active learning methods for named entity recognition in clinical text. J Biomed Inform. 2015;58:11\u20138.","journal-title":"J Biomed Inform"},{"key":"2117_CR58","unstructured":"Chaybouti S, Saghe A, Shabou A. EfficientQA\u202f: a RoBERTa based phrase-indexed question-answering system. 2021; figure 1:1\u20139."},{"key":"2117_CR59","unstructured":"shainaraza. bner-biobert. GitHub. 2022."},{"key":"2117_CR60","unstructured":"Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging. 2015."},{"key":"2117_CR61","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. In: Advances in neural information processing systems. 2017. p. 5998\u20136008."},{"key":"2117_CR62","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. 1997;9:1735\u201380.","journal-title":"Neural Comput"},{"key":"2117_CR63","doi-asserted-by":"crossref","unstructured":"Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C. Neural architectures for named entity recognition. Preprint http:\/\/arxiv.org\/abs\/160301360. 2016.","DOI":"10.18653\/v1\/N16-1030"},{"key":"2117_CR64","unstructured":"Lafferty J, Mccallum A, Pereira F. Conditional random fields\u202f: probabilistic models for segmenting and labeling sequence data abstract. 1999;2001:282\u20139"},{"key":"2117_CR65","unstructured":"Sexton T. IOB Format Intro\u2014Nestor. 2022."},{"key":"2117_CR66","doi-asserted-by":"publisher","first-page":"2629","DOI":"10.3389\/fneur.2021.811276","volume":"12","author":"L Gilio","year":"2022","unstructured":"Gilio L, Galifi G, Centonze D, Stampanoni-Bassi M. Case Report: overlap between long COVID and functional neurological disorders. Front Neurol. 2022;12:2629.","journal-title":"Front Neurol"},{"key":"2117_CR67","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s13256-022-03691-2","volume":"17","author":"HA El-naggar","year":"2023","unstructured":"El-naggar HA, El-Mahallawy YA, Harby MI, Abou Madawi NA. Bilateral collagenous fibroma of the hard palate: a case report and review of the literature. J Med Case Rep. 2023;17:5.","journal-title":"J Med Case Rep"},{"key":"2117_CR68","doi-asserted-by":"crossref","unstructured":"Nivre J, Scholz M. Deterministic dependency parsing of English text. In: COLING 2004: proceedings of the 20th international conference on computational linguistics. 2004. pp. 64\u201370.","DOI":"10.3115\/1220355.1220365"},{"key":"2117_CR69","unstructured":"Tang R, Nogueira R, Zhang E, Gupta N, Cam P, Cho K, et al. Rapidly bootstrapping a question answering dataset for COVID-19. 2020. arxiv:2004.11339"},{"key":"2117_CR70","unstructured":"huggingface. transformers. GitHub. 2022."},{"key":"2117_CR71","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1162\/tacl_a_00104","volume":"4","author":"JPC Chiu","year":"2016","unstructured":"Chiu JPC, Nichols E. Named entity recognition with bidirectional LSTM-CNNs. Trans Assoc Comput Linguist. 2016;4:357\u201370.","journal-title":"Trans Assoc Comput Linguist"},{"key":"2117_CR72","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.1093\/bioinformatics\/bty869","volume":"35","author":"X Wang","year":"2019","unstructured":"Wang X, Zhang Y, Ren X, Zhang Y, Zitnik M, Shang J, et al. Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics. 2019;35:1745\u201352.","journal-title":"Bioinformatics"},{"key":"2117_CR73","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1093\/bioinformatics\/btx761","volume":"34","author":"L Luo","year":"2018","unstructured":"Luo L, Yang Z, Yang P, Zhang Y, Wang L, Lin H, et al. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition. Bioinformatics. 2018;34:1381\u20138.","journal-title":"Bioinformatics"},{"key":"2117_CR74","unstructured":"Akbik A, Blythe D, Vollgraf R. Contextual string embeddings for sequence labeling. IN: COLING 2018 - 27th Int Conf Comput Linguist Proc. 2018. pp. 1638\u201349."},{"key":"2117_CR75","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1186\/s12859-019-2813-6","volume":"20","author":"W Yoon","year":"2019","unstructured":"Yoon W, So CH, Lee J, Kang J. Collabonet: collaboration of deep neural networks for biomedical named entity recognition. BMC Bioinform. 2019;20:55\u201365.","journal-title":"BMC Bioinform"},{"key":"2117_CR76","doi-asserted-by":"crossref","unstructured":"Beltagy I, Lo K, Cohan A. SCIBERT: A pretrained language model for scientific text. In: EMNLP-IJCNLP 2019 - 2019 conference on empirical methods in natural language processing and 9th international joint conference on natural language processing, proceedings of the conference, 2020. pp. 3615\u201320.","DOI":"10.18653\/v1\/D19-1371"},{"key":"2117_CR77","doi-asserted-by":"crossref","unstructured":"Peng Y, Yan S, Lu Z. Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets. Preprint http:\/\/arxiv.org\/abs\/190605474. 2019.","DOI":"10.18653\/v1\/W19-5006"},{"key":"2117_CR78","doi-asserted-by":"publisher","first-page":"2690","DOI":"10.3390\/app10082690","volume":"10","author":"C Quan","year":"2020","unstructured":"Quan C, Luo Z, Wang S. A hybrid deep learning model for protein\u2013protein interactions extraction from biomedical literature. Appl Sci. 2020;10:2690.","journal-title":"Appl Sci"},{"key":"2117_CR79","doi-asserted-by":"crossref","unstructured":"Wang L, Cao Z, De Melo G, Liu Z. Relation classification via multi-level attention cnns. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers). 2016. pp. 1298\u2013307.","DOI":"10.18653\/v1\/P16-1123"},{"key":"2117_CR80","first-page":"268","volume":"36","author":"J Singh","year":"2004","unstructured":"Singh J. Centers for disease control and prevention. Indian J Pharmacol. 2004;36:268\u20139.","journal-title":"Indian J Pharmacol"},{"key":"2117_CR81","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s12911-018-0627-5","volume":"18","author":"H-J Lee","year":"2018","unstructured":"Lee H-J, Zhang Y, Jiang M, Xu J, Tao C, Xu H. Identifying direct temporal relations between time and events from clinical notes. BMC Med Inform Decis Mak. 2018;18:49.","journal-title":"BMC Med Inform Decis Mak"},{"key":"2117_CR82","first-page":"280","volume":"105","author":"A Egdahl","year":"1954","unstructured":"Egdahl A. WHO: World Health Organization. Ill Med J. 1954;105:280\u20132.","journal-title":"Ill Med J"},{"key":"2117_CR83","doi-asserted-by":"publisher","DOI":"10.1007\/s15010-021-01666-x","author":"H Akbarialiabad","year":"2021","unstructured":"Akbarialiabad H, Taghrir MH, Abdollahi A, Ghahramani N, Kumar M, Paydar S, et al. Long COVID, a comprehensive systematic scoping review. Infection. 2021. https:\/\/doi.org\/10.1007\/s15010-021-01666-x.","journal-title":"Infection"},{"key":"2117_CR84","doi-asserted-by":"publisher","first-page":"2716","DOI":"10.1093\/jamia\/ocab170","volume":"28","author":"BG Patra","year":"2021","unstructured":"Patra BG, Sharma MM, Vekaria V, Adekkanattu P, Patterson OV, Glicksberg B, et al. Extracting social determinants of health from electronic health records using natural language processing: a systematic review. J Am Med Inform Assoc. 2021;28:2716\u201327.","journal-title":"J Am Med Inform Assoc"},{"key":"2117_CR85","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/S0306-4379(03)00072-3","volume":"29","author":"P-N Tan","year":"2004","unstructured":"Tan P-N, Kumar V, Srivastava J. Selecting the right objective measure for association analysis. Inf Syst. 2004;29:293\u2013313.","journal-title":"Inf Syst"},{"key":"2117_CR86","unstructured":"Rutherford A. How to argue with a racist: History, science, race and reality. UK: Hachette; 2020."},{"key":"2117_CR87","unstructured":"(OCR) O for CR. Methods for de-identification of PHI. HHS.gov. 2012."},{"key":"2117_CR88","doi-asserted-by":"publisher","DOI":"10.1002\/0470011815.b2a03072","author":"KJ Rothman","year":"2005","unstructured":"Rothman KJ, Greenland S. Hill\u2019s criteria for causality. Encycl Biostat. 2005. https:\/\/doi.org\/10.1002\/0470011815.b2a03072.","journal-title":"Encycl Biostat"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02117-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-023-02117-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02117-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T19:19:01Z","timestamp":1679512741000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-023-02117-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,26]]},"references-count":88,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["2117"],"URL":"https:\/\/doi.org\/10.1186\/s12911-023-02117-3","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,26]]},"assertion":[{"value":"25 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All data and methods were carried out in accordance with relevant guidelines and regulations. Public Health Ontario's (PHO) Ethics Review Board (ERB) waived the need for the ethical approval and informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"20"}}