{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T13:02:37Z","timestamp":1792587757907,"version":"4.5.0"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2019,2,6]],"date-time":"2019-02-06T00:00:00Z","timestamp":1549411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Reducing Health Disparities","award":["T32NR007969"],"award-info":[{"award-number":["T32NR007969"]}]},{"name":"Precision in Symptom Self-Management","award":["P30NR016587"],"award-info":[{"award-number":["P30NR016587"]}]},{"name":"Data Science Techniques","award":["K99NR017651"],"award-info":[{"award-number":["K99NR017651"]}]},{"name":"Microbial Function and Impaired Glucose Tolerance During Pregnancy","award":["F31NR017821"],"award-info":[{"award-number":["F31NR017821"]}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Natural language processing (NLP) of symptoms from electronic health records (EHRs) could contribute to the advancement of symptom science. We aim to synthesize the literature on the use of NLP to process or analyze symptom information documented in EHR free-text narratives.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Our search of 1964 records from PubMed and EMBASE was narrowed to 27 eligible articles. Data related to the purpose, free-text corpus, patients, symptoms, NLP methodology, evaluation metrics, and quality indicators were extracted for each study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Symptom-related information was presented as a primary outcome in 14 studies. EHR narratives represented various inpatient and outpatient clinical specialties, with general, cardiology, and mental health occurring most frequently. Studies encompassed a wide variety of symptoms, including shortness of breath, pain, nausea, dizziness, disturbed sleep, constipation, and depressed mood. NLP approaches included previously developed NLP tools, classification methods, and manually curated rule-based processing. Only one-third (n\u2009=\u20099) of studies reported patient demographic characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>NLP is used to extract information from EHR free-text narratives written by a variety of healthcare providers on an expansive range of symptoms across diverse clinical specialties. The current focus of this field is on the development of methods to extract symptom information and the use of symptom information for disease classification tasks rather than the examination of symptoms themselves.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Future NLP studies should concentrate on the investigation of symptoms and symptom documentation in EHR free-text narratives. Efforts should be undertaken to examine patient characteristics and make symptom-related NLP algorithms or pipelines and vocabularies openly available.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocy173","type":"journal-article","created":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T15:12:24Z","timestamp":1543331544000},"page":"364-379","source":"Crossref","is-referenced-by-count":399,"title":["Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review"],"prefix":"10.1093","volume":"26","author":[{"given":"Theresa A","family":"Koleck","sequence":"first","affiliation":[{"name":"School of Nursing, Columbia University, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caitlin","family":"Dreisbach","sequence":"additional","affiliation":[{"name":"School of Nursing, University of Virginia, Charlottesville, Virginia, USA"},{"name":"Data Science Institute, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip E","family":"Bourne","sequence":"additional","affiliation":[{"name":"Data Science Institute, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6202-6001","authenticated-orcid":false,"given":"Suzanne","family":"Bakken","sequence":"additional","affiliation":[{"name":"School of Nursing, Columbia University, New York, New York, USA"},{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, USA"},{"name":"Data Science Institute, Columbia University, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,2,6]]},"reference":[{"key":"2020110613062081000_ocy173-B1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.ijmedinf.2018.03.013","article-title":"Concurrence of big data analytics and healthcare: a systematic review","volume":"114","author":"Mehta","year":"2018","journal-title":"Int J Med Inform"},{"issue":"6","key":"2020110613062081000_ocy173-B2","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1001\/jamaoncol.2016.0213","article-title":"Natural language processing in oncology","volume":"2","author":"Yim","year":"2016","journal-title":"JAMA Oncol"},{"key":"2020110613062081000_ocy173-B3","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.ymeth.2015.01.015","article-title":"Application of text mining in the biomedical domain","volume":"74","author":"Fleuren","year":"2015","journal-title":"Methods"},{"key":"2020110613062081000_ocy173-B4","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jbi.2017.11.011","article-title":"Clinical information extraction applications: a literature review","volume":"77","author":"Wang","year":"2018","journal-title":"J Biomed Inform"},{"key":"2020110613062081000_ocy173-B5","year":"2003"},{"key":"2020110613062081000_ocy173-B6","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/978-1-4939-0709-0_15","article-title":"Mining the electronic health record for disease knowledge","volume":"1159","author":"Chen","year":"2014","journal-title":"Methods Mol Biol"},{"key":"2020110613062081000_ocy173-B7","first-page":": 97","article-title":"\u201cBig data\u201d and the electronic health record","volume":"9","author":"Ross","year":"2014","journal-title":"Yearb Med Inform"},{"issue":"5","key":"2020110613062081000_ocy173-B8","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1136\/amiajnl-2011-000203","article-title":"2010 i2b2\/VA challenge on concepts, assertions, and relations in clinical text","volume":"18","author":"Uzuner","year":"2011","journal-title":"J Am Med Inform Assoc"},{"key":"2020110613062081000_ocy173-B9","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1016\/j.jbi.2017.10.005","article-title":"A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry","volume":"75","author":"Uzuner","year":"2017","journal-title":"J Biomed Inform"},{"issue":"4","key":"2020110613062081000_ocy173-B10","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1197\/jamia.M3115","article-title":"Recognizing obesity and comorbidities in sparse data","volume":"16","author":"Uzuner","year":"2009","journal-title":"J Am Med Inform Assoc"},{"issue":"5","key":"2020110613062081000_ocy173-B11","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1136\/amiajnl-2013-001628","article-title":"Evaluating temporal relations in clinical text: 2012 i2b2 challenge","volume":"20","author":"Sun","year":"2013","journal-title":"J Am Med Inform Assoc"},{"key":"2020110613062081000_ocy173-B12","doi-asserted-by":"crossref","first-page":"S67","DOI":"10.1016\/j.jbi.2015.07.001","article-title":"Identifying risk factors for heart disease over time: overview of 2014 i2b2\/UTHealth shared task track 2","volume":"58","author":"Stubbs","year":"2015","journal-title":"J Biomed Inform"},{"issue":"4","key":"2020110613062081000_ocy173-B13","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.soncn.2016.08.004","article-title":"Cancer symptom cluster management","volume":"32","author":"Kwekkeboom","year":"2016","journal-title":"Semin Oncol Nurs"},{"key":"2020110613062081000_ocy173-B14","first-page":"67","article-title":"Sitting on pins and needles. Characterization of symptom descriptions in clinical notes","volume":"2013","author":"Forbush","year":"2013","journal-title":"AMIA Jt Summits Transl Sci Proc"},{"issue":"6","key":"2020110613062081000_ocy173-B15","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1093\/jamia\/ocx066","article-title":"Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review","volume":"24","author":"Canan","year":"2017","journal-title":"J Am Med Inform Assoc"},{"issue":"5","key":"2020110613062081000_ocy173-B16","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1093\/jamia\/ocv180","article-title":"Extracting information from the text of electronic medical records to improve case detection: a systematic review","volume":"23","author":"Ford","year":"2016","journal-title":"J Am Med Inform Assoc"},{"key":"2020110613062081000_ocy173-B17","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jbi.2017.07.012","article-title":"Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review","volume":"73","author":"Kreimeyer","year":"2017","journal-title":"J Biomed Inform"},{"issue":"2","key":"2020110613062081000_ocy173-B18","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1148\/radiol.16142770","article-title":"Natural language processing in radiology: a systematic review","volume":"279","author":"Pons","year":"2016","journal-title":"Radiology"},{"key":"2020110613062081000_ocy173-B19","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.jbi.2014.06.009","article-title":"Text summarization in the biomedical domain: a systematic review of recent research","volume":"52","author":"Mishra","year":"2014","journal-title":"J Biomed Inform"},{"issue":"12","key":"2020110613062081000_ocy173-B20","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/j.ijmedinf.2012.12.005","article-title":"Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records","volume":"83","author":"Byrd","year":"2014","journal-title":"Int J Med Inform"},{"key":"2020110613062081000_ocy173-B21","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s12911-017-0418-4","article-title":"Early recognition of multiple sclerosis using natural language processing of the electronic health record","volume":"17","author":"Chase","year":"2017","journal-title":"BMC Med Inform Decis Mak"},{"issue":"4","key":"2020110613062081000_ocy173-B22","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.jbi.2007.11.004","article-title":"Evaluation of preprocessing techniques for chief complaint classification","volume":"41","author":"Dara","year":"2008","journal-title":"J Biomed Inform"},{"key":"2020110613062081000_ocy173-B23","first-page":"356","article-title":"General symptom extraction from VA electronic medical notes","volume":"245","author":"Divita","year":"2017","journal-title":"Stud Health Technol Inform"},{"issue":"1_Part_1","key":"2020110613062081000_ocy173-B24","doi-asserted-by":"crossref","first-page":"11","DOI":"10.7326\/0003-4819-156-1-201201030-00003","article-title":"Comparison of natural language processing biosurveillance methods for identifying influenza from encounter notes","volume":"156","author":"Elkin","year":"2012","journal-title":"Ann Intern Med"},{"key":"2020110613062081000_ocy173-B25","first-page":"256","article-title":"Automating a severity score guideline for community-acquired pneumonia employing medical language processing of discharge summaries","author":"Friedman","year":"1999","journal-title":"Proc AMIA Symp"},{"issue":"3","key":"2020110613062081000_ocy173-B26","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1097\/MLR.0000000000000651","article-title":"A novel model for predicting rehospitalization risk incorporating physical function, cognitive status, and psychosocial support using natural language processing","volume":"55","author":"Greenwald","year":"2017","journal-title":"Med Care"},{"key":"2020110613062081000_ocy173-B27","first-page":"36","article-title":"Application of natural language processing to VA electronic health records to identify phenotypic characteristics for clinical and research purposes","volume":"2008","author":"Gundlapalli","year":"2008","journal-title":"Summit Transl Bioinform"},{"key":"2020110613062081000_ocy173-B28","doi-asserted-by":"crossref","first-page":"S39","DOI":"10.1016\/j.jbi.2016.07.012","article-title":"Detecting the presence of an indwelling urinary catheter and urinary symptoms in hospitalized patients using natural language processing","volume":"71S","author":"Gundlapalli","year":"2017","journal-title":"J Biomed Inform"},{"issue":"14","key":"2020110613062081000_ocy173-B29","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1016\/j.vaccine.2009.01.105","article-title":"Detecting possible vaccine adverse events in clinical notes of the electronic medical record","volume":"27","author":"Hazlehurst","year":"2009","journal-title":"Vaccine"},{"issue":"5","key":"2020110613062081000_ocy173-B30","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1136\/amiajnl-2012-001076","article-title":"Longitudinal analysis of pain in patients with metastatic prostate cancer using natural language processing of medical record text","volume":"20","author":"Heintzelman","year":"2013","journal-title":"J Am Med Inform Assoc"},{"key":"2020110613062081000_ocy173-B31","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1097\/NCN.0b013e3181a91b58","article-title":"Exploring the ability of natural language processing to extract data from nursing narratives","volume":"27","author":"Hyun","year":"2009","journal-title":"Comput Inform Nurs"},{"issue":"11","key":"2020110613062081000_ocy173-B32","doi-asserted-by":"crossref","first-page":"e0187121","DOI":"10.1371\/journal.pone.0187121","article-title":"ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records","volume":"12","author":"Iqbal","year":"2017","journal-title":"PLoS One"},{"issue":"1","key":"2020110613062081000_ocy173-B33","doi-asserted-by":"crossref","first-page":"e012012","DOI":"10.1136\/bmjopen-2016-012012","article-title":"Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project","volume":"7","author":"Jackson","year":"2017","journal-title":"BMJ Open"},{"issue":"5","key":"2020110613062081000_ocy173-B34","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1109\/TNB.2015.2422612","article-title":"Clinical documents clustering based on medication\/symptom names using multi-view nonnegative matrix factorization","volume":"14","author":"Ling","year":"2015","journal-title":"IEEE Trans Nanobioscience"},{"issue":"3","key":"2020110613062081000_ocy173-B35","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.ijmedinf.2011.11.005","article-title":"Detection of infectious symptoms from VA emergency department and primary care clinical documentation","volume":"81","author":"Matheny","year":"2012","journal-title":"Int J Med Inform"},{"issue":"6","key":"2020110613062081000_ocy173-B36","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1007\/s13300-017-0314-z","article-title":"Tolerability and effectiveness of exenatide once weekly relative to basal insulin among type 2 diabetes patients of different races in routine care","volume":"8","author":"Nunes","year":"2017","journal-title":"Diabetes Ther"},{"key":"2020110613062081000_ocy173-B37","first-page":"530","article-title":"Agreement between patient-reported symptoms and their documentation in the medical record","volume":"14","author":"Pakhomov","year":"2008","journal-title":"Am J Manag Care"},{"issue":"4","key":"2020110613062081000_ocy173-B38","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.ahj.2006.12.022","article-title":"Epidemiology of angina pectoris: role of natural language processing of the medical record","volume":"153","author":"Pakhomov","year":"2007","journal-title":"Am Heart J"},{"issue":"5","key":"2020110613062081000_ocy173-B39","doi-asserted-by":"crossref","first-page":"e007504","DOI":"10.1136\/bmjopen-2014-007504","article-title":"Mood instability is a common feature of mental health disorders and is associated with poor clinical outcomes","volume":"5","author":"Patel","year":"2015","journal-title":"BMJ Open"},{"issue":"3","key":"2020110613062081000_ocy173-B40","doi-asserted-by":"crossref","first-page":"e313","DOI":"10.1200\/JOP.2014.002741","article-title":"Detecting unplanned care from clinician notes in electronic health records","volume":"11","author":"Tamang","year":"2015","journal-title":"J Oncol Pract"},{"key":"2020110613062081000_ocy173-B41","doi-asserted-by":"crossref","first-page":"1178222617713018.","DOI":"10.1177\/1178222617713018","article-title":"Leveraging Food and Drug Administration Adverse Event Reports for the automated monitoring of electronic health records in a pediatric hospital","volume":"9","author":"Tang","year":"2017","journal-title":"Biomed Inform Insights"},{"issue":"7","key":"2020110613062081000_ocy173-B42","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.cardfail.2014.03.008","article-title":"Prevalence of heart failure signs and symptoms in a large primary care population identified through the use of text and data mining of the electronic health record","volume":"20","author":"Vijayakrishnan","year":"2014","journal-title":"J Card Fail"},{"key":"2020110613062081000_ocy173-B43","first-page":"783","article-title":"Automated knowledge acquisition from clinical narrative reports","volume":"2008","author":"Wang","year":"2008","journal-title":"AMIA Annu Symp Proc"},{"issue":"3","key":"2020110613062081000_ocy173-B44","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1197\/jamia.M3028","article-title":"Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study","volume":"16","author":"Wang","year":"2009","journal-title":"J Am Med Inform Assoc"},{"issue":"9","key":"2020110613062081000_ocy173-B45","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1513\/AnnalsATS.201602-131OC","article-title":"Natural kanguage processing to assess documentation of features of critical illness in discharge documents of acute respiratory distress syndrome survivors","volume":"13","author":"Weissman","year":"2016","journal-title":"Ann Am Thorac Soc"},{"key":"2020110613062081000_ocy173-B46","first-page":"629","article-title":"Identifying patients with depression using free-text clinical documents","volume":"216","author":"Zhou","year":"2015","journal-title":"Stud Health Technol Inform"},{"issue":"19","key":"2020110613062081000_ocy173-B47","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1093\/bioinformatics\/btu393","article-title":"circlize Implements and enhances circular visualization in R","volume":"30","author":"Gu","year":"2014","journal-title":"Bioinformatics"},{"key":"2020110613062081000_ocy173-B48","doi-asserted-by":"crossref","first-page":"101.","DOI":"10.1186\/s13059-015-0669-2","article-title":"When will \u2018open science\u2019 become simply \u2018science\u2019?","volume":"16","author":"Watson","year":"2015","journal-title":"Genome Biol"},{"key":"2020110613062081000_ocy173-B49","doi-asserted-by":"crossref","first-page":"372","DOI":"10.7554\/eLife.16800","article-title":"How open science helps researchers succeed","volume":"5","author":"McKiernan","year":"2016","journal-title":"Elife"},{"issue":"5","key":"2020110613062081000_ocy173-B50","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1136\/jamia.2009.001560","article-title":"Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications","volume":"17","author":"Savova","year":"2010","journal-title":"J Am Med Inform Assoc"},{"issue":"4","key":"2020110613062081000_ocy173-B51","doi-asserted-by":"crossref","first-page":"djw253","DOI":"10.1093\/jnci\/djw253","article-title":"Advancing symptom science through symptom cluster research: expert panel proceedings and recommendations","volume":"109","author":"Miaskowski","year":"2017","journal-title":"J Natl Cancer Inst"},{"issue":"4","key":"2020110613062081000_ocy173-B52","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc Neurol"},{"issue":"5","key":"2020110613062081000_ocy173-B53","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.outlook.2014.06.006","article-title":"Envisioning the future in symptom science","volume":"62","author":"Corwin","year":"2014","journal-title":"Nurs Outlook"},{"key":"2020110613062081000_ocy173-B54","author":"Rui"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/26\/4\/364\/34151341\/ocy173.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/26\/4\/364\/34151341\/ocy173.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T14:07:11Z","timestamp":1604671631000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/26\/4\/364\/5307912"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,6]]},"references-count":54,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2,6]]},"published-print":{"date-parts":[[2019,4,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocy173","relation":{},"ISSN":["1527-974X"],"issn-type":[{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2019,4]]},"published":{"date-parts":[[2019,2,6]]}}}