{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T11:43:10Z","timestamp":1784288590002,"version":"3.55.0"},"reference-count":150,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T00:00:00Z","timestamp":1639353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Computational Sepsis Mining and Modelling project through the Norwegian University of Science and Technology Health Strategic Area"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and methods<\/jats:title>\n                  <jats:p>PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocab236","type":"journal-article","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T19:24:31Z","timestamp":1633980271000},"page":"559-575","source":"Crossref","is-referenced-by-count":107,"title":["Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review"],"prefix":"10.1093","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8079-7923","authenticated-orcid":false,"given":"Melissa Y","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2709-3991","authenticated-orcid":false,"given":"Lise Tuset","family":"Gustad","sequence":"additional","affiliation":[{"name":"Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway"},{"name":"Department of Medicine, Levanger Hospital, Clinic of Medicine and Rehabilitation, Nord-Tr\u00f8ndelag Hospital Trust, Levanger, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8163-2362","authenticated-orcid":false,"given":"\u00d8ystein","family":"Nytr\u00f8","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,12,13]]},"reference":[{"issue":"8","key":"2022012920324170700_ocab236-B1","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (Sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"issue":"3","key":"2022012920324170700_ocab236-B2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1164\/rccm.201504-0781OC","article-title":"Assessment of global incidence and mortality of hospital-treated sepsis. Current estimates and limitations","volume":"193","author":"Fleischmann","year":"2016","journal-title":"Am J Respir Crit Care Med"},{"issue":"19","key":"2022012920324170700_ocab236-B3","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1056\/NEJMoa010307","article-title":"Early goal-directed therapy in the treatment of severe sepsis and septic shock","volume":"345","author":"Rivers","year":"2001","journal-title":"N Engl J Med"},{"key":"2022012920324170700_ocab236-B4","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1097\/01.CCM.0000217961.75225.E9","article-title":"Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock","volume":"34","author":"Kumar","year":"2006","journal-title":"Crit Care Med"},{"issue":"1","key":"2022012920324170700_ocab236-B5","doi-asserted-by":"crossref","first-page":"53","DOI":"10.5152\/eurasianjmed.2017.17062","article-title":"Sepsis and septic shock: current treatment strategies and new approaches","volume":"49","author":"Polat","year":"2017","journal-title":"Eurasian J Med"},{"issue":"16","key":"2022012920324170700_ocab236-B6","doi-asserted-by":"crossref","first-page":"3988","DOI":"10.1073\/pnas.1803551115","article-title":"News feature: the quest to solve sepsis","volume":"115","author":"Arnold","year":"2018","journal-title":"Proc Natl Acad Sci USA"},{"issue":"3","key":"2022012920324170700_ocab236-B7","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1152\/physrev.00037.2012","article-title":"Sepsis: multiple abnormalities, heterogeneous responses, and evolving understanding","volume":"93","author":"Iskander","year":"2013","journal-title":"Physiol Rev"},{"issue":"1","key":"2022012920324170700_ocab236-B8","doi-asserted-by":"crossref","first-page":"010404","DOI":"10.7189\/jogh.01.010404","article-title":"Assessing available information on the burden of sepsis: global estimates of incidence, prevalence and mortality","volume":"2","author":"Jawad","year":"2012","journal-title":"J Glob Health"},{"key":"2022012920324170700_ocab236-B9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2018.12.027","article-title":"Prediction of sepsis patients using machine learning approach: a meta-analysis","volume":"170","author":"Islam","year":"2019","journal-title":"Comput Methods Programs Biomed"},{"key":"2022012920324170700_ocab236-B10","doi-asserted-by":"crossref","first-page":"103488","DOI":"10.1016\/j.compbiomed.2019.103488","article-title":"Clinical applications of artificial intelligence in sepsis: a narrative review","volume":"115","author":"Schinkel","year":"2019","journal-title":"Comput Biol Med"},{"issue":"S 02","key":"2022012920324170700_ocab236-B11","doi-asserted-by":"crossref","first-page":"e43","DOI":"10.1055\/s-0039-1695717","article-title":"Clinical decision-support systems for detection of systemic inflammatory response syndrome, sepsis, and septic shock in critically Ill patients: a systematic review","volume":"58","author":"Wulff","year":"2019","journal-title":"Methods Inf Med"},{"issue":"3","key":"2022012920324170700_ocab236-B12","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1055\/s-0040-1710525","article-title":"A review of predictive analytics solutions for sepsis patients","volume":"11","author":"Teng","year":"2020","journal-title":"Appl Clin Inform"},{"issue":"3","key":"2022012920324170700_ocab236-B13","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s00134-019-05872-y","article-title":"Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy","volume":"46","author":"Fleuren","year":"2020","journal-title":"Intensive Care Med"},{"key":"2022012920324170700_ocab236-B14","doi-asserted-by":"crossref","first-page":"617486","DOI":"10.3389\/fmed.2021.617486","article-title":"Early detection of sepsis with machine learning techniques: a brief clinical perspective","volume":"8","author":"Giacobbe","year":"2021","journal-title":"Front Med (Lausanne)"},{"key":"2022012920324170700_ocab236-B15","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3389\/fmed.2019.00066","article-title":"The revival of the notes field: leveraging the unstructured content in electronic health records","volume":"6","author":"Assale","year":"2019","journal-title":"Front Med (Lausanne)"},{"key":"2022012920324170700_ocab236-B16","doi-asserted-by":"crossref","DOI":"10.1002\/wics.1549","article-title":"Challenges and opportunities beyond structured data in analysis of electronic health records","volume":"13","author":"Tayefi","year":"2021","journal-title":"Wiley Interdiscip Rev Comput Stat"},{"issue":"2","key":"2022012920324170700_ocab236-B17","doi-asserted-by":"crossref","first-page":"e12239","DOI":"10.2196\/12239","article-title":"Natural language processing of clinical notes on chronic diseases: systematic review","volume":"7","author":"Sheikhalishahi","year":"2019","journal-title":"JMIR Med Inform"},{"key":"2022012920324170700_ocab236-B18","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":"2022012920324170700_ocab236-B19","doi-asserted-by":"crossref","first-page":"103301","DOI":"10.1016\/j.jbi.2019.103301","article-title":"A frame semantic overview of NLP-based information extraction for cancer-related EHR notes","volume":"100","author":"Datta","year":"2019","journal-title":"J Biomed Inform"},{"issue":"1","key":"2022012920324170700_ocab236-B20","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"},{"key":"2022012920324170700_ocab236-B21","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":"3","key":"2022012920324170700_ocab236-B22","doi-asserted-by":"crossref","first-page":"e17984","DOI":"10.2196\/17984","article-title":"Clinical text data in machine learning: systematic review","volume":"8","author":"Spasic","year":"2020","journal-title":"JMIR Med Inform"},{"key":"2022012920324170700_ocab236-B23","first-page":"172","article-title":"NLP-based identification of pneumonia cases from free-text radiological reports","volume":"2008","author":"Elkin","year":"2008","journal-title":"AMIA Annu Symp Proc"},{"key":"2022012920324170700_ocab236-B24","doi-asserted-by":"crossref","first-page":"46226","DOI":"10.1038\/srep46226","article-title":"Analysis of free text in electronic health records for identification of cancer patient trajectories","volume":"7","author":"Jensen","year":"2017","journal-title":"Sci Rep"},{"issue":"5","key":"2022012920324170700_ocab236-B25","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":"2022012920324170700_ocab236-B26","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jbi.2016.03.008","article-title":"Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods","volume":"61","author":"Soguero-Ruiz","year":"2016","journal-title":"J Biomed Inform"},{"key":"2022012920324170700_ocab236-B27","doi-asserted-by":"crossref","first-page":"7988","DOI":"10.1109\/ACCESS.2016.2618775","article-title":"Predicting complications in critical care using heterogeneous clinical data","volume":"4","author":"Huddar","year":"2016","journal-title":"IEEE Access"},{"key":"2022012920324170700_ocab236-B28","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.ejca.2020.11.030","article-title":"Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)\/HER2-negative advanced breast cancer patients","volume":"144","author":"Ribelles","year":"2021","journal-title":"Eur J Cancer"},{"key":"2022012920324170700_ocab236-B29","first-page":"237","article-title":"Comparing methods for identifying pancreatic cancer patients using electronic data sources","volume":"2010","author":"Friedlin","year":"2010","journal-title":"AMIA Annu Symp Proc"},{"key":"2022012920324170700_ocab236-B30","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.ijmedinf.2019.06.028","article-title":"Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression","volume":"129","author":"Van Vleck","year":"2019","journal-title":"Int J Med Inform"},{"issue":"8","key":"2022012920324170700_ocab236-B31","doi-asserted-by":"crossref","first-page":"e70944","DOI":"10.1371\/journal.pone.0070944","article-title":"Using the electronic medical record to identify community-acquired pneumonia: toward a replicable automated strategy","volume":"8","author":"DeLisle","year":"2013","journal-title":"PLoS One"},{"key":"2022012920324170700_ocab236-B32","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"},{"issue":"7","key":"2022012920324170700_ocab236-B33","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1097\/MIB.0b013e31828133fd","article-title":"Improving case definition of Crohn\u2019s disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach","volume":"19","author":"Ananthakrishnan","year":"2013","journal-title":"Inflamm Bowel Dis"},{"issue":"e1","key":"2022012920324170700_ocab236-B34","doi-asserted-by":"crossref","first-page":"e162","DOI":"10.1136\/amiajnl-2011-000583","article-title":"Portability of an algorithm to identify rheumatoid arthritis in electronic health records","volume":"19","author":"Carroll","year":"2012","journal-title":"J Am Med Inform Assoc"},{"issue":"8","key":"2022012920324170700_ocab236-B35","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1002\/acr.20184","article-title":"Electronic medical records for discovery research in rheumatoid arthritis","volume":"62","author":"Liao","year":"2010","journal-title":"Arthritis Care Res (Hoboken)"},{"key":"2022012920324170700_ocab236-B36","first-page":"189","article-title":"Na\u00efve electronic health record phenotype identification for Rheumatoid arthritis","volume":"2011","author":"Carroll","year":"2011","journal-title":"AMIA Annu Symp Proc"},{"issue":"11","key":"2022012920324170700_ocab236-B37","doi-asserted-by":"crossref","first-page":"e78927","DOI":"10.1371\/journal.pone.0078927","article-title":"Modeling disease severity in multiple sclerosis using electronic health records","volume":"8","author":"Xia","year":"2013","journal-title":"PLoS One"},{"issue":"10","key":"2022012920324170700_ocab236-B38","doi-asserted-by":"crossref","first-page":"e13377","DOI":"10.1371\/journal.pone.0013377","article-title":"Combining free text and structured electronic medical record entries to detect acute respiratory infections","volume":"5","author":"DeLisle","year":"2010","journal-title":"PLoS One"},{"issue":"7","key":"2022012920324170700_ocab236-B39","doi-asserted-by":"crossref","first-page":"e100845","DOI":"10.1371\/journal.pone.0100845","article-title":"Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement","volume":"9","author":"Zheng","year":"2014","journal-title":"PLoS One"},{"issue":"4","key":"2022012920324170700_ocab236-B40","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1016\/j.chest.2016.06.020","article-title":"Identifying patients with sepsis on the hospital wards","volume":"151","author":"Bhattacharjee","year":"2017","journal-title":"Chest"},{"issue":"6","key":"2022012920324170700_ocab236-B41","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1097\/JHQ.0000000000000066","article-title":"Automated detection of sepsis using electronic medical record data: a systematic review","volume":"39","author":"Despins","year":"2017","journal-title":"J Healthc Qual"},{"issue":"5","key":"2022012920324170700_ocab236-B42","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.cmi.2019.09.009","article-title":"Machine learning for clinical decision support in infectious diseases: a narrative review of current applications","volume":"26","author":"Peiffer-Smadja","year":"2020","journal-title":"Clin Microbiol Infect"},{"issue":"2","key":"2022012920324170700_ocab236-B43","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jhin.2012.11.031","article-title":"Advances in electronic surveillance for healthcare-associated infections in the 21st century: a systematic review","volume":"84","author":"Freeman","year":"2013","journal-title":"J Hosp Infect"},{"issue":"5","key":"2022012920324170700_ocab236-B44","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1136\/amiajnl-2013-002089","article-title":"Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review","volume":"21","author":"de Bruin","year":"2014","journal-title":"J Am Med Inform Assoc"},{"issue":"10","key":"2022012920324170700_ocab236-B45","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1016\/j.cmi.2020.02.003","article-title":"Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies","volume":"26","author":"Luz","year":"2020","journal-title":"Clin Microbiol Infect"},{"issue":"7","key":"2022012920324170700_ocab236-B46","doi-asserted-by":"crossref","first-page":"e1000097","DOI":"10.1371\/journal.pmed.1000097","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement","volume":"6","author":"Moher","year":"2009","journal-title":"PLoS Med"},{"issue":"4","key":"2022012920324170700_ocab236-B47","doi-asserted-by":"crossref","first-page":"e0174708","DOI":"10.1371\/journal.pone.0174708","article-title":"Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning","volume":"12","author":"Horng","year":"2017","journal-title":"PLoS One"},{"key":"2022012920324170700_ocab236-B48","first-page":"257","author":"Apostolova","year":"2017"},{"key":"2022012920324170700_ocab236-B49","author":"Culliton"},{"key":"2022012920324170700_ocab236-B50","first-page":"6103","article-title":"Natural language processing of clinical notes for improved early prediction of septic shock in the ICU","volume":"2019","author":"Liu","year":"2019","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"issue":"4","key":"2022012920324170700_ocab236-B51","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.annemergmed.2018.11.036","article-title":"Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis","volume":"73","author":"Delahanty","year":"2019","journal-title":"Ann Emerg Med"},{"issue":"1","key":"2022012920324170700_ocab236-B52","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1038\/s41467-021-20910-4","article-title":"Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare","volume":"12","author":"Goh","year":"2021","journal-title":"Nat Commun"},{"key":"2022012920324170700_ocab236-B53","first-page":"197","article-title":"Contextual embeddings from clinical notes improves prediction of sepsis","volume":"2020","author":"Amrollahi","year":"2020","journal-title":"AMIA Annu Symp Proc"},{"key":"2022012920324170700_ocab236-B54","author":"Hammoud","year":"30\u2013 3, 2020;    ,"},{"key":"2022012920324170700_ocab236-B55","author":"Qin","year":"2021"},{"issue":"1","key":"2022012920324170700_ocab236-B56","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1086\/502115","article-title":"An intranet-based automated system for the surveillance of nosocomial infections: prospective validation compared with physicians\u2019 self-reports","volume":"24","author":"Bouam","year":"2003","journal-title":"Infect Control Hosp Epidemiol"},{"issue":"Pt 1","key":"2022012920324170700_ocab236-B57","first-page":"432","article-title":"Electronic surveillance of healthcare-associated infections with MONI-ICU\u2013a clinical breakthrough compared to conventional surveillance systems","volume":"160","author":"Koller","year":"2010","journal-title":"Stud Health Technol Inform"},{"issue":"2","key":"2022012920324170700_ocab236-B58","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.jbi.2006.06.003","article-title":"Automated identification of adverse events related to central venous catheters","volume":"40","author":"Penz","year":"2007","journal-title":"J Biomed Inform"},{"key":"2022012920324170700_ocab236-B59","first-page":"35","author":"Proux","year":"2009"},{"issue":"1","key":"2022012920324170700_ocab236-B60","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.jhin.2011.05.006","article-title":"Automated detection of nosocomial infections: evaluation of different strategies in an intensive care unit 2000-2006","volume":"79","author":"Bouzbid","year":"2011","journal-title":"J Hosp Infect"},{"key":"2022012920324170700_ocab236-B61","first-page":"1171","author":"Jo","year":"2015"},{"key":"2022012920324170700_ocab236-B62","author":"Wang","year":"2018"},{"key":"2022012920324170700_ocab236-B63","doi-asserted-by":"crossref","first-page":"103915","DOI":"10.1016\/j.ijmedinf.2019.06.022","article-title":"Automatic classification of free-text medical causes from death certificates for reactive mortality surveillance in France","volume":"131","author":"Baghdadi","year":"2019","journal-title":"Int J Med Inform"},{"key":"2022012920324170700_ocab236-B64","first-page":"1","article-title":"A time-critical topic model for predicting the survival time of sepsis patients","volume":"2020","author":"Guo","year":"2020","journal-title":"Sci Program"},{"key":"2022012920324170700_ocab236-B65","first-page":"45","author":"Ribas Ripoll"},{"issue":"4","key":"2022012920324170700_ocab236-B66","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1093\/jamia\/ocw011","article-title":"Electronic medical record phenotyping using the anchor and learn framework","volume":"23","author":"Halpern","year":"2016","journal-title":"J Am Med Inform Assoc"},{"key":"2022012920324170700_ocab236-B67","first-page":"2529","article-title":"Visualizing patient journals by combining vital signs monitoring and natural language processing","volume":"2016","author":"Vilic","year":"2016","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"issue":"1","key":"2022012920324170700_ocab236-B68","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1186\/s13104-021-05529-4","article-title":"Embedding, aligning and reconstructing clinical notes to explore sepsis","volume":"14","author":"Zhu","year":"2021","journal-title":"BMC Res Notes"},{"issue":"1","key":"2022012920324170700_ocab236-B69","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jinf.2012.10.004","article-title":"Needles and the damage done: reasons for admission and financial costs associated with injecting drug use in a Central London Teaching Hospital","volume":"66","author":"Marks","year":"2013","journal-title":"J Infect"},{"issue":"8","key":"2022012920324170700_ocab236-B70","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1177\/0148607114536580","article-title":"Utility of electronic medical records to assess the relationship between parenteral nutrition and central line-associated bloodstream infections in adult hospitalized patients","volume":"39","author":"Ippolito","year":"2015","journal-title":"JPEN J Parenter Enteral Nutr"},{"issue":"1","key":"2022012920324170700_ocab236-B71","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1186\/s12879-020-05330-x","article-title":"A surveillance method to identify patients with sepsis from electronic health records in Hong Kong: a single centre retrospective study","volume":"20","author":"Liu","year":"2020","journal-title":"BMC Infect Dis"},{"issue":"8","key":"2022012920324170700_ocab236-B72","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1001\/jama.2011.1204","article-title":"Automated identification of postoperative complications within an electronic medical record using natural language processing","volume":"306","author":"Murff","year":"2011","journal-title":"JAMA"},{"issue":"6","key":"2022012920324170700_ocab236-B73","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1097\/MLR.0b013e31828d1210","article-title":"Exploring the frontier of electronic health record surveillance: the case of postoperative complications","volume":"51","author":"FitzHenry","year":"2013","journal-title":"Med Care"},{"key":"2022012920324170700_ocab236-B74","article-title":"A keyword approach to identify adverse events within narrative documents from 4 Italian institutions","author":"Piscitelli","journal-title":"J Patient Saf"},{"key":"2022012920324170700_ocab236-B75","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.jcrc.2020.01.007","article-title":"Automated screening of natural language in electronic health records for the diagnosis septic shock is feasible and outperforms an approach based on explicit administrative codes","volume":"56","author":"Vermassen","year":"2020","journal-title":"J Crit Care"},{"issue":"5","key":"2022012920324170700_ocab236-B76","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1097\/CCM.0b013e31820a92c6","article-title":"Multiparameter intelligent monitoring in intensive care II: a public-access intensive care unit database","volume":"39","author":"Saeed","year":"2011","journal-title":"Crit Care Med"},{"key":"2022012920324170700_ocab236-B77","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci Data"},{"issue":"299","key":"2022012920324170700_ocab236-B78","doi-asserted-by":"crossref","first-page":"299ra122","DOI":"10.1126\/scitranslmed.aab3719","article-title":"A targeted real-time early warning score (TREWScore) for septic shock","volume":"7","author":"Henry","year":"2015","journal-title":"Sci Transl Med"},{"issue":"7","key":"2022012920324170700_ocab236-B79","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1097\/00003246-200107000-00002","article-title":"Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care","volume":"29","author":"Angus","year":"2001","journal-title":"Crit Care Med"},{"issue":"13","key":"2022012920324170700_ocab236-B80","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1001\/jama.2017.13836","article-title":"Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014","volume":"318","author":"Rhee","year":"2017","journal-title":"JAMA"},{"key":"2022012920324170700_ocab236-B81","first-page":"1","author":"Reyna","year":"2019"},{"key":"2022012920324170700_ocab236-B82","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/B978-012369378-5\/50025-9","article-title":"Chief complaints and ICD codes","author":"Wagner","year":"2006","journal-title":"Handbook of Biosurveillance"},{"key":"2022012920324170700_ocab236-B83","first-page":"263","article-title":"History and physical examination","author":"Lucian","year":"2016","journal-title":"Murray and Nadel\u2019s Textbook of Respiratory Medicine"},{"key":"2022012920324170700_ocab236-B84","first-page":"12","author":"Aghili","year":"1997"},{"issue":"1","key":"2022012920324170700_ocab236-B85","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1111\/j.1365-2702.2005.01235.x","article-title":"Information handling in the nursing discharge note","volume":"15","author":"Helles\u00f8","year":"2006","journal-title":"J Clin Nurs"},{"key":"2022012920324170700_ocab236-B86","article-title":"Nursing documentation and recording systems of nursing care","volume":"(4","author":"Ioanna","year":"2007","journal-title":"Health Sci J"},{"issue":"6","key":"2022012920324170700_ocab236-B87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5334\/ijic.3003","article-title":"Planning for the discharge, not for patient self-management at home - an observational and interview study of hospital discharge","volume":"17","author":"Flink","year":"2017","journal-title":"Int J Integr Care"},{"key":"2022012920324170700_ocab236-B88","doi-asserted-by":"crossref","first-page":"h2696","DOI":"10.1136\/sbmj.h2696","article-title":"How to write a discharge summary","volume":"351","author":"Stopford","year":"2015","journal-title":"BMJ"},{"issue":"4","key":"2022012920324170700_ocab236-B89","first-page":"112","article-title":"Does the electronic patient record support the discharge process? A study on physicians\u2019 use of clinical information systems during discharge of patients with coronary heart disease","volume":"34","author":"S\u00f8rby","year":"2006","journal-title":"Health Inf Manag"},{"key":"2022012920324170700_ocab236-B90","volume-title":"Advances in Patient Safety: New Directions and Alternative Approaches (Vol. 2: Culture and Redesign. AHRQ Publication No. 08-0034-2)","author":"Kind","year":"2011"},{"issue":"3","key":"2022012920324170700_ocab236-B91","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1111\/j.1471-6712.2007.00534.x","article-title":"Comparison of nurses\u2019 and physicians\u2019 documentation of functional abilities of older patients in acute care\u2013patient records compared with standardized assessment","volume":"22","author":"Jensd\u00f3ttir","year":"2008","journal-title":"Scand J Caring Sci"},{"key":"2022012920324170700_ocab236-B92","first-page":"130","article-title":"Use of electronic health record documentation by healthcare workers in an acute care hospital system","volume":"59","author":"Penoyer","year":"2014","journal-title":"J Healthc Manag"},{"issue":"1","key":"2022012920324170700_ocab236-B93","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s12913-018-3025-x","article-title":"Procedural and documentation variations in intravenous infusion administration: a mixed methods study of policy and practice across 16 hospital trusts in England","volume":"18","author":"Furniss","year":"2018","journal-title":"BMC Health Serv Res"},{"issue":"3","key":"2022012920324170700_ocab236-B94","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1093\/jamia\/ocx138","article-title":"Clinical documentation variations and NLP system portability: a case study in asthma birth cohorts across institutions","volume":"25","author":"Sohn","year":"2018","journal-title":"J Am Med Inform Assoc"},{"key":"2022012920324170700_ocab236-B95","first-page":"9","article-title":"International differences in medical care practices","volume":"1989","author":"McPherson","year":"1989","journal-title":"Health Care Financ Rev"},{"issue":"7","key":"2022012920324170700_ocab236-B96","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/S0196-0644(85)80887-8","article-title":"Organization structure and the performance of hospital emergency services","volume":"14","author":"Georgopoulos","year":"1985","journal-title":"Ann Emerg Med"},{"issue":"6","key":"2022012920324170700_ocab236-B97","doi-asserted-by":"crossref","first-page":"2182","DOI":"10.1111\/j.1475-6773.2006.00595.x","article-title":"How do doctors in different countries manage the same patient? Results of a factorial experiment","volume":"41","author":"McKinlay","year":"2006","journal-title":"Health Serv Res"},{"issue":"6","key":"2022012920324170700_ocab236-B98","doi-asserted-by":"crossref","first-page":"e144-51","DOI":"10.1016\/j.ijmedinf.2009.08.003","article-title":"Analysis of communicative behaviour: profiling roles and activities","volume":"79","author":"S\u00f8rby","year":"2010","journal-title":"Int J Med Inform"},{"issue":"3","key":"2022012920324170700_ocab236-B99","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1504\/IJDMB.2020.107877","article-title":"Identifying catheter-related events through sentence classification","volume":"23","author":"R\u00f8st","year":"2020","journal-title":"Int J Data Min Bioinform"},{"issue":"2\u20133","key":"2022012920324170700_ocab236-B100","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1080\/00437956.1954.11659520","article-title":"Distributional Structure","volume":"10","author":"Harris","year":"1954","journal-title":"Word World"},{"key":"2022012920324170700_ocab236-B101","author":"Le","year":"2014"},{"key":"2022012920324170700_ocab236-B102","first-page":"993","article-title":"Latent Dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J Mach Learn Res"},{"key":"2022012920324170700_ocab236-B103","author":"Mikolov","year":"2013"},{"key":"2022012920324170700_ocab236-B104","first-page":"1532","author":"Pennington","year":"2014"},{"key":"2022012920324170700_ocab236-B105","author":"Mikolov","year":"2013"},{"key":"2022012920324170700_ocab236-B106","first-page":"4171","author":"Devlin","year":"2019"},{"key":"2022012920324170700_ocab236-B107","first-page":"72","author":"Alsentzer","year":"2019"},{"key":"2022012920324170700_ocab236-B108","volume-title":"Probabilistic Graphical Models: Principles and Techniques","author":"Koller","year":"2009"},{"issue":"3","key":"2022012920324170700_ocab236-B109","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"key":"2022012920324170700_ocab236-B110","first-page":"21","author":"Fix","year":"1951"},{"issue":"1","key":"2022012920324170700_ocab236-B111","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2022012920324170700_ocab236-B112","first-page":"1189","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","author":"Friedman","year":"1999","journal-title":"Ann Stat"},{"issue":"4","key":"2022012920324170700_ocab236-B113","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput Stat Data Anal"},{"issue":"6088","key":"2022012920324170700_ocab236-B114","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"2022012920324170700_ocab236-B115","doi-asserted-by":"crossref","first-page":"318","DOI":"10.7551\/mitpress\/5236.001.0001","volume-title":"Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations","author":"Rumelhart","year":"1986"},{"key":"2022012920324170700_ocab236-B116","author":"Mikolov","year":"2010"},{"key":"2022012920324170700_ocab236-B117","author":"Cho","year":"2014"},{"issue":"8","key":"2022012920324170700_ocab236-B118","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"issue":"11","key":"2022012920324170700_ocab236-B119","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1016\/j.mayocp.2014.07.009","article-title":"Severe sepsis and septic shock: clinical overview and update on management","volume":"89","author":"Cawcutt","year":"2014","journal-title":"Mayo Clin Proc"},{"key":"2022012920324170700_ocab236-B120","doi-asserted-by":"crossref","first-page":"16045","DOI":"10.1038\/nrdp.2016.45","article-title":"Sepsis and septic shock","volume":"2","author":"Hotchkiss","year":"2016","journal-title":"Nat Rev Dis Primers"},{"issue":"6","key":"2022012920324170700_ocab236-B121","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1378\/chest.101.6.1644","article-title":"Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP\/SCCM Consensus Conference Committee. American College of Chest Physicians\/Society of Critical Care Medicine","volume":"101","author":"Bone","year":"1992","journal-title":"Chest"},{"issue":"7","key":"2022012920324170700_ocab236-B122","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1007\/BF01709751","article-title":"The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction\/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine","volume":"22","author":"Vincent","year":"1996","journal-title":"Intensive Care Med"},{"issue":"8","key":"2022012920324170700_ocab236-B123","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1001\/jama.2016.0288","article-title":"Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (Sepsis-3)","volume":"315","author":"Seymour","year":"2016","journal-title":"JAMA"},{"issue":"10","key":"2022012920324170700_ocab236-B124","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1093\/qjmed\/94.10.521","article-title":"Validation of a modified early warning score in medical admissions","volume":"94","author":"Subbe","year":"2001","journal-title":"QJM"},{"issue":"3","key":"2022012920324170700_ocab236-B125","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1007\/s00134-017-4683-6","article-title":"Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016","volume":"43","author":"Rhodes","year":"2017","journal-title":"Intensive Care Med"},{"issue":"2","key":"2022012920324170700_ocab236-B126","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1097\/CCM.0b013e31827e83af","article-title":"Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012","volume":"41","author":"Dellinger","year":"2013","journal-title":"Crit Care Med"},{"issue":"5","key":"2022012920324170700_ocab236-B127","doi-asserted-by":"crossref","first-page":"e1002022","DOI":"10.1371\/journal.pmed.1002022","article-title":"The clinical challenge of sepsis identification and monitoring","volume":"13","author":"Vincent","year":"2016","journal-title":"PLoS Med"},{"issue":"1","key":"2022012920324170700_ocab236-B128","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1186\/s13054-019-2475-9","article-title":"In-hospital mortality associated with the misdiagnosis or unidentified site of infection at admission","volume":"23","author":"Abe","year":"2019","journal-title":"Crit Care"},{"issue":"6","key":"2022012920324170700_ocab236-B129","doi-asserted-by":"crossref","first-page":"380","DOI":"10.7326\/M13-1419","article-title":"Variation in diagnostic coding of patients with pneumonia and its association with hospital risk-standardized mortality rates: a cross-sectional analysis","volume":"160","author":"Rothberg","year":"2014","journal-title":"Ann Intern Med"},{"issue":"5","key":"2022012920324170700_ocab236-B130","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1183\/09031936.00104808","article-title":"Pneumonia in the context of severe sepsis: a significant diagnostic problem","volume":"32","author":"Bewick","year":"2008","journal-title":"Eur Respir J"},{"issue":"1","key":"2022012920324170700_ocab236-B131","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1093\/cid\/ciu750","article-title":"Comparison of trends in sepsis incidence and coding using administrative claims versus objective clinical data","volume":"60","author":"Rhee","year":"2015","journal-title":"Clin Infect Dis"},{"issue":"1","key":"2022012920324170700_ocab236-B132","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S2213-2600(20)30520-8","article-title":"Sepsis: the importance of an accurate final diagnosis","volume":"9","author":"Tidswell","year":"2021","journal-title":"Lancet Respir Med"},{"key":"2022012920324170700_ocab236-B133","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40560-019-0368-2","article-title":"Physician agreement on the diagnosis of sepsis in the intensive care unit: estimation of concordance and analysis of underlying factors in a multicenter cohort","volume":"7","author":"Lopansri","year":"2019","journal-title":"J Intensive Care"},{"key":"2022012920324170700_ocab236-B134","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s13054-016-1266-9","article-title":"Diagnosing sepsis is subjective and highly variable: a survey of intensivists using case vignettes","volume":"20","author":"Rhee","year":"2016","journal-title":"Crit Care"},{"issue":"Suppl 1","key":"2022012920324170700_ocab236-B135","doi-asserted-by":"crossref","first-page":"S89","DOI":"10.21037\/jtd.2019.12.51","article-title":"Sepsis trends: increasing incidence and decreasing mortality, or changing denominator?","volume":"12","author":"Rhee","year":"2020","journal-title":"J Thorac Dis"},{"issue":"4","key":"2022012920324170700_ocab236-B136","doi-asserted-by":"crossref","first-page":"e433","DOI":"10.1097\/CCM.0000000000004875","article-title":"Comparison of sepsis definitions as automated criteria","volume":"49","author":"Yu","year":"2021","journal-title":"Crit Care Med"},{"issue":"1","key":"2022012920324170700_ocab236-B137","doi-asserted-by":"crossref","first-page":"6145","DOI":"10.1038\/s41598-019-42637-5","article-title":"Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the ICU","volume":"9","author":"Liu","year":"2019","journal-title":"Sci Rep"},{"issue":"8","key":"2022012920324170700_ocab236-B138","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1097\/CCM.0000000000003184","article-title":"Mortality measures to profile hospital performance for patients with septic shock","volume":"46","author":"Walkey","year":"2018","journal-title":"Crit Care Med"},{"issue":"3","key":"2022012920324170700_ocab236-B139","doi-asserted-by":"crossref","first-page":"56","DOI":"10.21037\/atm.2017.01.49","article-title":"The challenge of early identification of the hospital patient at risk of septic complications","volume":"5","author":"Vincent","year":"2017","journal-title":"Ann Transl Med"},{"issue":"4","key":"2022012920324170700_ocab236-B140","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.jbi.2005.02.003","article-title":"Extracting information on pneumonia in infants using natural language processing of radiology reports","volume":"38","author":"Mendon\u00e7a","year":"2005","journal-title":"J Biomed Inform"},{"issue":"8","key":"2022012920324170700_ocab236-B141","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1002\/pds.3418","article-title":"Natural language processing to identify pneumonia from radiology reports","volume":"22","author":"Dublin","year":"2013","journal-title":"Pharmacoepidemiol Drug Saf"},{"issue":"3","key":"2022012920324170700_ocab236-B142","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":"3","key":"2022012920324170700_ocab236-B143","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1093\/jamia\/ocz200","article-title":"Deep learning in clinical natural language processing: a methodical review","volume":"27","author":"Wu","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"2022012920324170700_ocab236-B144","doi-asserted-by":"crossref","first-page":"100057","DOI":"10.1016\/j.yjbinx.2019.100057","article-title":"A survey of word embeddings for clinical text","volume":"100","author":"Khattak","year":"2019","journal-title":"J Biomed Informatics: X"},{"issue":"1","key":"2022012920324170700_ocab236-B145","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1055\/s-0038-1667080","article-title":"Expanding the diversity of texts and applications: findings from the section on clinical natural language processing of the international medical informatics association yearbook","volume":"27","author":"N\u00e9v\u00e9ol","year":"2018","journal-title":"Yearb Med Inform"},{"issue":"4","key":"2022012920324170700_ocab236-B146","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1093\/jamia\/ocy173","article-title":"Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review","volume":"26","author":"Koleck","year":"2019","journal-title":"J Am Med Inform Assoc"},{"issue":"10","key":"2022012920324170700_ocab236-B147","doi-asserted-by":"crossref","first-page":"e48307","DOI":"10.1371\/journal.pone.0048307","article-title":"Chronic medical conditions and risk of sepsis","volume":"7","author":"Wang","year":"2012","journal-title":"PLoS One"},{"issue":"1","key":"2022012920324170700_ocab236-B148","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1513\/AnnalsATS.201806-391OC","article-title":"Paths into sepsis: trajectories of presepsis healthcare use","volume":"16","author":"Prescott","year":"2019","journal-title":"Ann Am Thorac Soc"},{"issue":"1","key":"2022012920324170700_ocab236-B149","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1111\/imr.12499","article-title":"The immune system\u2019s role in sepsis progression, resolution, and long-term outcome","volume":"274","author":"Delano","year":"2016","journal-title":"Immunol Rev"},{"issue":"4","key":"2022012920324170700_ocab236-B150","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1097\/CCM.0000000000002967","article-title":"Development and external validation of an automated computer-aided risk score for predicting sepsis in emergency medical admissions using the patient\u2019s first electronically recorded vital signs and blood test results","volume":"46","author":"Faisal","year":"2018","journal-title":"Crit Care Med"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/29\/3\/559\/42333223\/ocab236.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/29\/3\/559\/42333223\/ocab236.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T20:35:06Z","timestamp":1643488506000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/29\/3\/559\/6460282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,13]]},"references-count":150,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,12,13]]},"published-print":{"date-parts":[[2022,1,29]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocab236","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,3,1]]},"published":{"date-parts":[[2021,12,13]]}}}