{"id":"https://openalex.org/W2941222833","doi":null,"title":"Scalable Electronic Phenotyping For Studying Patient Comorbidities.","display_name":"Scalable Electronic Phenotyping For Studying Patient Comorbidities.","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2941222833","mag":"2941222833","pmid":"https://pubmed.ncbi.nlm.nih.gov/30815116","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/6371288"},"language":"en","primary_location":{"id":"pmh:oai:europepmc.org:5377684","doi":null,"is_oa":true,"landing_page_url":"http://europepmc.org/pmc/articles/PMC6371288","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"","raw_type":"Text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://europepmc.org/pmc/articles/PMC6371288","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037639769","display_name":"Albee Yun Ling","orcid":"https://orcid.org/0000-0002-3814-5307","observed_orcids":["https://orcid.org/0000-0002-3814-5307"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Albee Y Ling","raw_affiliation_strings":["Biomedical Informatics Training Program, Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Informatics Training Program, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039868033","display_name":"Emily Alsentzer","orcid":"https://orcid.org/0000-0002-5370-1746","observed_orcids":["https://orcid.org/0000-0002-5370-1746"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Emily Alsentzer","raw_affiliation_strings":["Biomedical Informatics Training Program, Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Informatics Training Program, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081986582","display_name":"Josephine Chen","orcid":"https://orcid.org/0009-0009-5269-1927","observed_orcids":["https://orcid.org/0009-0009-5269-1927"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Josephine Chen","raw_affiliation_strings":["Biomedical Informatics Training Program, Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Informatics Training Program, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011590035","display_name":"Juan M. Banda","orcid":"https://orcid.org/0000-0001-8499-824X","observed_orcids":["https://orcid.org/0000-0001-8499-824X","https://orcid.org/0000-0003-3864-0241"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juan M Banda","raw_affiliation_strings":["Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA","Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]},{"raw_affiliation_string":"Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA#TAB#","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036575460","display_name":"Suzanne R Tamang","orcid":"https://orcid.org/0000-0003-2077-4620","observed_orcids":["https://orcid.org/0000-0003-2077-4620"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suzanne Tamang","raw_affiliation_strings":["Department of Biomedical Data Science, Stanford University, Stanford, CA","Dept of Biomedical Data Science, Stanford Univ, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Data Science, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]},{"raw_affiliation_string":"Dept of Biomedical Data Science, Stanford Univ, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009606643","display_name":"Evan P. Minty","orcid":"https://orcid.org/0000-0003-4631-9992","observed_orcids":["https://orcid.org/0000-0003-4631-9992"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Evan Minty","raw_affiliation_strings":["Biomedical Informatics Training Program, Stanford University, Stanford, CA","Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Informatics Training Program, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]},{"raw_affiliation_string":"Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2018","issue":null,"first_page":"740","last_page":"749"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12246","display_name":"Chronic Disease Management Strategies","score":0.0044999998062849045,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.003700000001117587,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/electronic-phenotyping","display_name":"electronic phenotyping","score":0.8930000066757202},{"id":"https://openalex.org/keywords/chronic-disease","display_name":"chronic disease","score":0.44999998807907104},{"id":"https://openalex.org/keywords/myocardial-infarction","display_name":"myocardial infarction","score":0.8560000061988831},{"id":"https://openalex.org/keywords/type-2-diabetes","display_name":"type 2 diabetes","score":0.9919999837875366},{"id":"https://openalex.org/keywords/icd-9-codes","display_name":"ICD-9 codes","score":0.8339999914169312},{"id":"https://openalex.org/keywords/lasso-logistic-regression","display_name":"LASSO logistic regression","score":0.8539999723434448},{"id":"https://openalex.org/keywords/training-sample-size","display_name":"training sample size","score":0.6769999861717224},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"supervised learning","score":0.5609999895095825}],"concepts":[{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6916954517364502},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6053903698921204},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6002888083457947},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5890410542488098},{"id":"https://openalex.org/C2779159551","wikidata":"https://www.wikidata.org/wiki/Q1414874","display_name":"Comorbidity","level":2,"score":0.5860676169395447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5681776404380798},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5527297258377075},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.5432814359664917},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5101471543312073},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4782123267650604},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4666125178337097},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.45890161395072937},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.38675981760025024},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2656015455722809},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.1737317144870758},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13189810514450073},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D002908","descriptor_name":"Chronic Disease","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000150","qualifier_name":"complications","is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009203","descriptor_name":"Myocardial Infarction","qualifier_ui":"Q000150","qualifier_name":"complications","is_major_topic":true},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015897","descriptor_name":"Comorbidity","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016015","descriptor_name":"Logistic Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D038801","descriptor_name":"International Classification of Diseases","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":4,"locations":[{"id":"pmh:oai:europepmc.org:5377684","doi":null,"is_oa":true,"landing_page_url":"http://europepmc.org/pmc/articles/PMC6371288","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"","raw_type":"Text"},{"id":"pmid:30815116","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30815116","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:6371288","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC6371288/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AMIA Annu Symp Proc","raw_type":"Text"},{"id":"mag:2941222833","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30815116","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"PubMed","raw_type":null}],"best_oa_location":{"id":"pmh:oai:europepmc.org:5377684","doi":null,"is_oa":true,"landing_page_url":"http://europepmc.org/pmc/articles/PMC6371288","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.98580002784729,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W34678172","https://openalex.org/W1126991912","https://openalex.org/W1512404644","https://openalex.org/W1582456500","https://openalex.org/W1596932914","https://openalex.org/W1779612606","https://openalex.org/W1808652302","https://openalex.org/W1851421563","https://openalex.org/W1895978999","https://openalex.org/W1983141380","https://openalex.org/W2018312500","https://openalex.org/W2019286098","https://openalex.org/W2057780602","https://openalex.org/W2080433173","https://openalex.org/W2102195380","https://openalex.org/W2107435951","https://openalex.org/W2129168200","https://openalex.org/W2139865360","https://openalex.org/W2154674126","https://openalex.org/W2331425312","https://openalex.org/W2337688125","https://openalex.org/W2356882517","https://openalex.org/W2786221479"],"related_works":["https://openalex.org/W2356882517","https://openalex.org/W2799774807","https://openalex.org/W17929806","https://openalex.org/W2070356350","https://openalex.org/W2591848905","https://openalex.org/W2963174312","https://openalex.org/W3113372606","https://openalex.org/W4309199631","https://openalex.org/W7155547568","https://openalex.org/W2606287704"],"abstract_inverted_index":{"Over":[0],"75":[1],"million":[2],"Americans":[3],"have":[4],"multiple":[5],"concurrent":[6],"chronic":[7],"conditions":[8],"and":[9,60,92,125],"medical":[10],"decision":[11],"making":[12],"for":[13,44,132],"these":[14],"patients":[15,29],"is":[16],"mostly":[17],"based":[18],"on":[19,98],"retrospective":[20],"cohort":[21],"studies.":[22],"Current":[23],"methods":[24],"to":[25,112],"generate":[26],"cohorts":[27,65],"of":[28,84,89,94,135],"with":[30,150],"comorbidities":[31],"are":[32],"neither":[33],"scalable":[34],"nor":[35],"generalizable.":[36],"We":[37,120],"propose":[38],"a":[39,151],"supervised":[40],"machine":[41],"learning":[42,45],"algorithm":[43],"comorbidity":[46],"phenotypes":[47],"without":[48],"requiring":[49],"manually":[50],"created":[51,114],"training":[52,85,127,153],"sets.":[53],"First,":[54],"we":[55,80,108],"generated":[56],"myocardial":[57],"infarction":[58],"(MI)":[59],"type-2":[61],"diabetes":[62],"(T2DM)":[63],"patient":[64],"using":[66,115],"ICD9-based":[67],"imperfectly":[68],"labeled":[69],"samples":[70],"upon":[71],"which":[72],"LASSO":[73],"logistic":[74],"regression":[75],"models":[76,113],"were":[77],"trained.":[78],"Second,":[79],"assessed":[81],"the":[82,133],"effects":[83],"sample":[86,128,154],"size,":[87],"inclusion":[88,93],"physician":[90],"input,":[91],"clinical":[95,136,146],"text":[96,137,147],"features":[97,149],"model":[99,144],"performance.":[100],"Using":[101],"ICD9":[102],"codes":[103],"as":[104,117,148],"our":[105,141],"labeling":[106,118],"heuristic,":[107],"achieved":[109],"comparable":[110],"performance":[111],"keywords":[116],"heuristic.":[119],"found":[121],"that":[122],"expert":[123],"input":[124],"higher":[126],"sizes":[129],"could":[130],"compensate":[131],"lack":[134],"derived":[138],"features.":[139],"However,":[140],"best":[142],"performing":[143],"included":[145],"large":[152],"size.":[155]},"counts_by_year":[],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2019-05-03T00:00:00"}