{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T08:06:44Z","timestamp":1783757204977,"version":"3.55.0"},"reference-count":69,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring. Informaticians and domain experts were recruited to design an annotation guideline and generate a training corpus of annotated EC for 230 Alzheimer\u2019s clinical trials, which were represented as queries against the OMOP CDM and included 8008 entities, 3550 attributes, and 3529 relations. A sequence labeling\u2013based method was developed for automatic entity and attribute recognition. Negation detection was supported by NegEx and a set of predefined rules. Relation extraction was achieved by a support vector machine classifier. We further performed terminology-based concept normalization and output structuring.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In task-specific evaluations, the best F1 score for entity recognition was 0.79, and for relation extraction was 0.89. The accuracy of negation detection was 0.94. The overall accuracy for query formalization was 0.71 in an end-to-end evaluation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study presents EliIE, an OMOP CDM\u2013based information extraction system for automatic structuring and formalization of free-text EC. According to our evaluation, machine learning-based EliIE outperforms existing systems and shows promise to improve.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocx019","type":"journal-article","created":{"date-parts":[[2017,3,3]],"date-time":"2017-03-03T04:10:28Z","timestamp":1488514228000},"page":"1062-1071","source":"Crossref","is-referenced-by-count":86,"title":["EliIE: An open-source information extraction system for clinical trial eligibility criteria"],"prefix":"10.1093","volume":"24","author":[{"given":"Tian","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaodian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youlan","family":"Tang","sequence":"additional","affiliation":[{"name":"Institute of Human Nutrition, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory W","family":"Hruby","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Rusanov","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"No\u00e9mie","family":"Elhadad","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhua","family":"Weng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,4,1]]},"reference":[{"issue":"4","key":"2020110612445299300_ocx019-B1","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/S0197-2456(96)00236-X","article-title":"Recruitment for controlled clinical trials: literature summary and annotated bibliography","volume":"18","author":"Lovato","year":"1997","journal-title":"Controlled Clinical Trials."},{"issue":"1","key":"2020110612445299300_ocx019-B2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/1745-6215-7-9","article-title":"What influences recruitment to randomised controlled trials? 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trials","volume":"54","author":"He","year":"2015","journal-title":"J Biomed Inform."},{"key":"2020110612445299300_ocx019-B6","first-page":"569","article-title":"Assessing the collective population representativeness of related type 2 diabetes trials by combining public data from ClinicalTrials.gov and NHANES","volume":"216","author":"He","year":"2015","journal-title":"Stud Health Technol Inform."},{"issue":"2","key":"2020110612445299300_ocx019-B7","doi-asserted-by":"crossref","first-page":"463","DOI":"10.4338\/ACI-2013-12-RA-0105","article-title":"Distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records","volume":"5","author":"Weng","year":"2014","journal-title":"Appl Clin Inform."},{"issue":"8","key":"2020110612445299300_ocx019-B8","doi-asserted-by":"crossref","first-page":"635","DOI":"10.7326\/M15-1460","article-title":"The ADAPTABLE Trial and PCORnet: Shining Light on a New Research 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Disambiguation","author":"Huang"},{"key":"2020110612445299300_ocx019-B43","article-title":"Combining Neural Networks and Log-linear Models to Improve Relation Extraction","author":"Nguyen"},{"key":"2020110612445299300_ocx019-B44","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D15-1206","article-title":"Classifying relations via long short term memory networks along shortest dependency paths","volume-title":"Proceedings of Conference on Empirical Methods in Natural Language Processing","author":"Xu","year":"2015"},{"key":"2020110612445299300_ocx019-B45","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.18653\/v1\/P16-1105","article-title":"End-to-end Relation Extraction using LSTMs on Sequences and Tree Structures","author":"Miwa","year":"2016","journal-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics"},{"key":"2020110612445299300_ocx019-B46","article-title":"Efficient Estimation of Word Representations in Vector Space","author":"Mikolov"},{"key":"2020110612445299300_ocx019-B47","first-page":"473","article-title":"Bidirectional RNN for medical event detection in electronic health records","volume-title":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","author":"Jagannatha","year":"2016"},{"key":"2020110612445299300_ocx019-B48","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.jbi.2016.02.011","article-title":"Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models","volume":"60","author":"Zhang","year":"2016","journal-title":"J Biomed Inform."},{"key":"2020110612445299300_ocx019-B49","article-title":"Structuring clinical trial eligibility criteria with common data model","volume-title":"Proc of 2015 AMIA Joint Summits for Translational Science","author":"Levy-Fix","year":"2015"},{"key":"2020110612445299300_ocx019-B50","first-page":"238","article-title":"Biological nomenclatures: a source of lexical knowledge and ambiguity","volume-title":"Proceedings of the Pacific Symposium of Biocomputing","author":"Tuason","year":"2003"},{"issue":"3","key":"2020110612445299300_ocx019-B51","doi-asserted-by":"crossref","first-page":"266","DOI":"10.3414\/ME15-01-0112","article-title":"Valx: a system for extracting and structuring numeric lab test comparison statements from text","volume":"55","author":"Hao","year":"2016","journal-title":"Methods Inform Med."},{"key":"2020110612445299300_ocx019-B52","author":"National Institutes of Health"},{"issue":"3","key":"2020110612445299300_ocx019-B53","first-page":"332","article-title":"Alzheimer\u2019s disease facts and figures","volume":"11","author":"Alzheimer\u2019s Association","year":"2015","journal-title":"Alzheimer\u2019s Dementia."},{"issue":"2","key":"2020110612445299300_ocx019-B54","first-page":"217","article-title":"The UMLS Metathesaurus: representing different views of biomedical concepts","volume":"81","author":"Schuyler","year":"1993","journal-title":"Bull Med Library Assoc."},{"key":"2020110612445299300_ocx019-B55","first-page":"102","article-title":"BRAT: a web-based tool for NLP-assisted text annotation","volume-title":"Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics","author":"Stenetorp","year":"2012"},{"key":"2020110612445299300_ocx019-B56","unstructured":"Kudo T . CRF++: Yet another CRF toolkit. Software.http:\/\/crfpp\/. Sourceforge. Net, 2005."},{"key":"2020110612445299300_ocx019-B57","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3115\/1225403.1225421","article-title":"NLTK: the natural language toolkit","volume-title":"Proceedings of the COLING\/ACL on Interactive Presentation Sessions","author":"Bird","year":"2006"},{"key":"2020110612445299300_ocx019-B58","first-page":"17","article-title":"Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program","volume-title":"Proceedings of the AMIA Symposium","author":"Aronson","year":"2001"},{"key":"2020110612445299300_ocx019-B59","first-page":"384","article-title":"Word representations: a simple and general method for semi-supervised learning","volume-title":"Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics","author":"Turian","year":"2010"},{"key":"2020110612445299300_ocx019-B60","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J Machine Learning Res."},{"key":"2020110612445299300_ocx019-B61","first-page":"289","article-title":"Probabilistic latent semantic analysis","volume-title":"Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence","author":"Hofmann","year":"1999"},{"issue":"4","key":"2020110612445299300_ocx019-B62","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/0031-3203(92)90088-Z","article-title":"A practical application of simulated annealing to clustering","volume":"25","author":"Brown","year":"1992","journal-title":"Pattern Recognition."},{"key":"2020110612445299300_ocx019-B63","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"Collobert","year":"2011","journal-title":"J Machine Learning Res."},{"key":"2020110612445299300_ocx019-B64","doi-asserted-by":"crossref","first-page":"240403","DOI":"10.1155\/2014\/240403","article-title":"Evaluating word representation features in biomedical named entity recognition tasks","volume":"2014","author":"Tang","year":"2014","journal-title":"BioMed Res Int."},{"issue":"5","key":"2020110612445299300_ocx019-B65","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1006\/jbin.2001.1029","article-title":"A simple algorithm for identifying negated findings and diseases in discharge summaries","volume":"34","author":"Chapman","year":"2001","journal-title":"J Biomed Inform."},{"issue":"3","key":"2020110612445299300_ocx019-B66","first-page":"27","article-title":"LIBSVM: a library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Transact Intell Syst Technol."},{"key":"2020110612445299300_ocx019-B67","first-page":"455","article-title":"Tumor information extraction in radiology reports for hepatocellular carcinoma patients","volume-title":"American Medical Informatics Association Summit on Clinical Research Informatics","author":"Yim","year":"2016"},{"key":"2020110612445299300_ocx019-B68","article-title":"CliNER: A Lightweight Tool for Clinical Named Entity Recognition","author":"Boag","year":"2015","journal-title":"AMIA Joint Summits on Clinical Research Informatics (poster)"},{"key":"2020110612445299300_ocx019-B69","first-page":"687","article-title":"Initial readability assessment of clinical trial eligibility criteria","volume":"2015","author":"Kang","year":"2015","journal-title":"AMIA Annu Symp Proc."}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/24\/6\/1062\/34149469\/ocx019.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/24\/6\/1062\/34149469\/ocx019.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T03:34:05Z","timestamp":1658720045000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/24\/6\/1062\/3098256"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,1]]},"references-count":69,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,4,1]]},"published-print":{"date-parts":[[2017,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocx019","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2017,11]]},"published":{"date-parts":[[2017,4,1]]}}}