{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T22:09:37Z","timestamp":1781993377730,"version":"3.54.5"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"e1","funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health grants","doi-asserted-by":"crossref","award":["U54-HG007963"],"award-info":[{"award-number":["U54-HG007963"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health grants","doi-asserted-by":"crossref","award":["U54-LM008748"],"award-info":[{"award-number":["U54-LM008748"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health grants","doi-asserted-by":"crossref","award":["R01-GM079330"],"award-info":[{"award-number":["R01-GM079330"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health grants","doi-asserted-by":"crossref","award":["K08-AR060257"],"award-info":[{"award-number":["K08-AR060257"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health grants","doi-asserted-by":"crossref","award":["K23- DK097142"],"award-info":[{"award-number":["K23- DK097142"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,4,1]]},"abstract":"<jats:p>Objective: Phenotyping algorithms are capable of accurately identifying patients with specific phenotypes from within electronic medical records systems. However, developing phenotyping algorithms in a scalable way remains a challenge due to the extensive human resources required. This paper introduces a high-throughput unsupervised feature selection method, which improves the robustness and scalability of electronic medical record phenotyping without compromising its accuracy.<\/jats:p>\n               <jats:p>Methods: The proposed Surrogate-Assisted Feature Extraction (SAFE) method selects candidate features from a pool of comprehensive medical concepts found in publicly available knowledge sources. The target phenotype\u2019s International Classification of Diseases, Ninth Revision and natural language processing counts, acting as noisy surrogates to the gold-standard labels, are used to create silver-standard labels. Candidate features highly predictive of the silver-standard labels are selected as the final features.<\/jats:p>\n               <jats:p>Results: Algorithms were trained to identify patients with coronary artery disease, rheumatoid arthritis, Crohn\u2019s disease, and ulcerative colitis using various numbers of labels to compare the performance of features selected by SAFE, a previously published automated feature extraction for phenotyping procedure, and domain experts. The out-of-sample area under the receiver operating characteristic curve and F-score from SAFE algorithms were remarkably higher than those from the other two, especially at small label sizes.<\/jats:p>\n               <jats:p>Conclusion: SAFE advances high-throughput phenotyping methods by automatically selecting a succinct set of informative features for algorithm training, which in turn reduces overfitting and the needed number of gold-standard labels. SAFE also potentially identifies important features missed by automated feature extraction for phenotyping or experts.<\/jats:p>","DOI":"10.1093\/jamia\/ocw135","type":"journal-article","created":{"date-parts":[[2016,9,16]],"date-time":"2016-09-16T03:53:42Z","timestamp":1473998022000},"page":"e143-e149","source":"Crossref","is-referenced-by-count":75,"title":["Surrogate-assisted feature extraction for high-throughput phenotyping"],"prefix":"10.1093","volume":"24","author":[{"given":"Sheng","family":"Yu","sequence":"first","affiliation":[{"name":"Center for Statistical Science, Tsinghua University, Beijing, China"},{"name":"Department of Industrial Engineering, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhishek","family":"Chakrabortty","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine P","family":"Liao","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Brigham and Women\u2019s Hospital, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianrun","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Radiology, Brigham and Women\u2019s Hospital, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashwin N","family":"Ananthakrishnan","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vivian S","family":"Gainer","sequence":"additional","affiliation":[{"name":"Research IS and Computing, Partners HealthCare, Charlestown, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susanne E","family":"Churchill","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Szolovits","sequence":"additional","affiliation":[{"name":"Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shawn N","family":"Murphy","sequence":"additional","affiliation":[{"name":"Research IS and Computing, Partners HealthCare, Charlestown, Massachusetts, USA"},{"name":"Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isaac S","family":"Kohane","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianxi","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2016,9,15]]},"reference":[{"key":"2020110612435374400_ocw135-B1","doi-asserted-by":"crossref","first-page":"4401","DOI":"10.1002\/sim.5620","article-title":"Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership","volume":"31","author":"Ryan","year":"2012","journal-title":"Stat Med."},{"key":"2020110612435374400_ocw135-B2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/clpt.2011.83","article-title":"Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels","volume":"90","author":"Tatonetti","year":"2011","journal-title":"Clin Pharmacol Ther."},{"key":"2020110612435374400_ocw135-B3","doi-asserted-by":"crossref","first-page":"f288","DOI":"10.1136\/bmj.f288","article-title":"QT interval and antidepressant use: a cross sectional study of electronic health records","volume":"346","author":"Castro","year":"2013","journal-title":"BMJ."},{"key":"2020110612435374400_ocw135-B4","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1002\/pds.3413","article-title":"Comparative effectiveness research using electronic health records: impacts of oral antidiabetic drugs on the development of chronic kidney disease","volume":"22","author":"L. Masica","year":"2013","journal-title":"Pharmacoepidemiol Drug Saf."},{"key":"2020110612435374400_ocw135-B5","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s00592-008-0090-3","article-title":"The risk of developing coronary artery disease or congestive heart failure, and overall mortality, in type 2 diabetic patients receiving rosiglitazone, pioglitazone, metformin, or sulfonylureas: a retrospective analysis","volume":"46","author":"Pantalone","year":"2009","journal-title":"Acta Diabetol."},{"key":"2020110612435374400_ocw135-B6","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1111\/j.1464-5491.2012.03577.x","article-title":"The risk of overall mortality in patients with Type 2 diabetes receiving different combinations of sulfonylureas and metformin: a retrospective analysis","volume":"29","author":"Pantalone","year":"2012","journal-title":"Diabet Med."},{"key":"2020110612435374400_ocw135-B7","doi-asserted-by":"crossref","first-page":"d1642","DOI":"10.1136\/bmj.d1642","article-title":"Effect of statin treatment on short term mortality after pneumonia episode: cohort study","volume":"342","author":"Douglas","year":"2011","journal-title":"BMJ."},{"key":"2020110612435374400_ocw135-B8","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.1109\/IEMBS.2010.5627691","article-title":"Secondary use of EHR data for correlated comorbidity prevalence estimate","volume-title":"2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Stakic","year":"2010"},{"key":"2020110612435374400_ocw135-B9","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1016\/j.jpsychires.2011.06.012","article-title":"Substance use disorders and comorbid Axis I and II psychiatric disorders among young psychiatric patients: findings from a large electronic health records database","volume":"45","author":"Wu","year":"2011","journal-title":"J Psychiatr Res."},{"key":"2020110612435374400_ocw135-B10","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/nrg2999","article-title":"Using electronic health records to drive discovery in disease genomics","volume":"12","author":"Kohane","year":"2011","journal-title":"Nat Rev Genet."},{"key":"2020110612435374400_ocw135-B11","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1002\/art.37801","article-title":"Associations of autoantibodies, autoimmune risk alleles, and clinical diagnoses from the electronic medical records in rheumatoid arthritis cases and non\u2013rheumatoid arthritis controls","volume":"65","author":"Liao","year":"2013","journal-title":"Arthritis Rheum."},{"key":"2020110612435374400_ocw135-B12","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1093\/bioinformatics\/btq126","article-title":"PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene\u2013disease associations","volume":"26","author":"Denny","year":"2010","journal-title":"Bioinformatics."},{"key":"2020110612435374400_ocw135-B13","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.ajhg.2011.09.008","article-title":"Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies","volume":"89","author":"Denny","year":"2011","journal-title":"Am J Hum Genet."},{"key":"2020110612435374400_ocw135-B14","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1038\/nbt.2749","article-title":"Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data","volume":"31","author":"Denny","year":"2013","journal-title":"Nat Biotechnol."},{"issue":"13","key":"2020110612435374400_ocw135-B15","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1161\/CIRCULATIONAHA.112.000604","article-title":"Genome- and phenome-wide analysis of cardiac conduction identifies markers of arrhythmia risk","volume":"127","author":"Ritchie","year":"2013","journal-title":"Circulation."},{"key":"2020110612435374400_ocw135-B16","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1136\/amiajnl-2010-000061","article-title":"Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience","volume":"18","author":"Pathak","year":"2011","journal-title":"J Am Med Inform Assoc"},{"key":"2020110612435374400_ocw135-B17","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1212\/WNL.49.3.660","article-title":"Inaccuracy of the International Classification of Diseases (ICD-9-CM) in identifying the diagnosis of ischemic cerebrovascular disease","volume":"49","author":"Benesch","year":"1997","journal-title":"Neurology."},{"key":"2020110612435374400_ocw135-B18","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1097\/01.mlr.0000160417.39497.a9","article-title":"Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors","volume":"43","author":"Birman-Deych","year":"2005","journal-title":"Med Care."},{"key":"2020110612435374400_ocw135-B19","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.thromres.2010.03.009","article-title":"Evaluation of the predictive value of ICD-9-CM coded administrative data for venous thromboembolism in the United States","volume":"126","author":"White","year":"2010","journal-title":"Thromb Res."},{"key":"2020110612435374400_ocw135-B20","first-page":"326","article-title":"The validity of ICD-9-CM codes in identifying postoperative deep vein thrombosis and pulmonary embolism","volume":"33","author":"Zhan","year":"2007","journal-title":"Jt Comm J Qual Patient Saf."},{"key":"2020110612435374400_ocw135-B21","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/1755-8794-4-13","article-title":"The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies","volume":"4","author":"McCarty","year":"2011","journal-title":"BMC Med Genomics."},{"key":"2020110612435374400_ocw135-B22","first-page":"274","article-title":"Analyzing the heterogeneity and complexity of electronic health record oriented phenotyping algorithms","volume":"2011","author":"Conway","year":"2011","journal-title":"AMIA Annu Symp Proc."},{"key":"2020110612435374400_ocw135-B23","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."},{"key":"2020110612435374400_ocw135-B24","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1097\/MIB.0b013e31828133fd","article-title":"Improving case definition of Crohn's 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."},{"key":"2020110612435374400_ocw135-B25","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."},{"key":"2020110612435374400_ocw135-B26","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1186\/s12958-015-0115-z","article-title":"Identification of subjects with polycystic ovary syndrome using electronic health records","volume":"13","author":"Castro","year":"2015","journal-title":"Reprod Biol Endocrinol."},{"key":"2020110612435374400_ocw135-B27","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1176\/appi.ajp.2014.14030423","article-title":"Validation of electronic health record phenotyping of bipolar disorder cases and controls","volume":"172","author":"Castro","year":"2014","journal-title":"Am J Psychiatry."},{"key":"2020110612435374400_ocw135-B28","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.jbi.2014.08.001","article-title":"Classification of CT pulmonary angiography reports by presence, chronicity, and location of pulmonary embolism with natural language processing","volume":"52","author":"Yu","year":"2014","journal-title":"J Biomed Inform."},{"key":"2020110612435374400_ocw135-B29","doi-asserted-by":"crossref","first-page":"e0136651","DOI":"10.1371\/journal.pone.0136651","article-title":"Methods to develop an electronic medical record phenotype algorithm to compare the risk of coronary artery disease across 3 chronic disease cohorts","volume":"10","author":"Liao","year":"2015","journal-title":"PLoS ONE."},{"key":"2020110612435374400_ocw135-B30","doi-asserted-by":"crossref","first-page":"h1885","DOI":"10.1136\/bmj.h1885","article-title":"Development of phenotype algorithms using electronic medical records and incorporating natural language processing","volume":"350","author":"Liao","year":"2015","journal-title":"BMJ."},{"key":"2020110612435374400_ocw135-B31","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."},{"key":"2020110612435374400_ocw135-B32","first-page":"170","article-title":"The UMLS project: making the conceptual connection between users and the information they need","volume":"81","author":"Humphreys","year":"1993","journal-title":"Bull Med Libr Assoc."},{"key":"2020110612435374400_ocw135-B33","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3115\/1118958.1118970","article-title":"Identification of patients with congestive heart failure using a binary classifier: a case study","volume-title":"Proceedings of the ACL 2003 Workshop on Natural Language Processing in Biomedicine, Volume 13","author":"Pakhomov","year":"2003"},{"key":"2020110612435374400_ocw135-B34","doi-asserted-by":"crossref","DOI":"10.1136\/amiajnl-2011-000752","article-title":"Pneumonia identification using statistical feature selection","author":"Bejan","year":"2012","journal-title":"J Am Med Inform Assoc."},{"key":"2020110612435374400_ocw135-B35","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"},{"key":"2020110612435374400_ocw135-B36","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Hastie","year":"2009"},{"issue":"5","key":"2020110612435374400_ocw135-B37","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1093\/jamia\/ocv034","article-title":"Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources","volume":"22","author":"Yu","year":"2015","journal-title":"J Am Med Inform Assoc."},{"issue":"12_S","key":"2020110612435374400_ocw135-B38","article-title":"Natural language processing improves phenotypic accuracy in an electronic medical record cohort of type 2 diabetes and cardiovascular disease","volume":"63","author":"Kumar","year":"2014","journal-title":"J Am Coll Cardio."},{"key":"2020110612435374400_ocw135-B39","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.semarthrit.2010.05.002","article-title":"Validation of psoriatic arthritis diagnoses in electronic medical records using natural language processing","volume":"40","author":"Love","year":"2011","journal-title":"Semin Arthritis Rheum."},{"key":"2020110612435374400_ocw135-B40","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J R Stat Soc Ser B."},{"key":"2020110612435374400_ocw135-B41","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1214\/08-AOS625","article-title":"On the adaptive elastic-net with a diverging number of parameters","volume":"37","author":"Zou","year":"2009","journal-title":"Ann Stat."},{"key":"2020110612435374400_ocw135-B42","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit Lett"},{"key":"2020110612435374400_ocw135-B43","author":"HITEx Manual"},{"key":"2020110612435374400_ocw135-B44","author":"Yu","year":"2013"},{"key":"2020110612435374400_ocw135-B45","doi-asserted-by":"crossref","first-page":"S14","DOI":"10.1038\/527S14a","article-title":"Deep phenotyping: The details of disease","volume":"527","author":"Delude","year":"2015","journal-title":"Nature."}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/24\/e1\/e143\/34149618\/ocw135.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/24\/e1\/e143\/34149618\/ocw135.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T18:16:23Z","timestamp":1604686583000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/24\/e1\/e143\/2631516"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,15]]},"references-count":45,"journal-issue":{"issue":"e1","published-online":{"date-parts":[[2016,9,15]]},"published-print":{"date-parts":[[2017,4,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocw135","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2017,4]]},"published":{"date-parts":[[2016,9,15]]}}}