{"id":"https://openalex.org/W3030197026","doi":null,"title":"Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.","display_name":"Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3030197026","mag":"3030197026","pmid":"https://pubmed.ncbi.nlm.nih.gov/32477672","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/7233082"},"language":"en","primary_location":{"id":"pmh:oai:pubmedcentral.nih.gov:7233082","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7233082","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 Jt Summits Transl Sci Proc","raw_type":"Text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7233082","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068424949","display_name":"Suraj Rajendran","orcid":"https://orcid.org/0000-0002-8149-0157","observed_orcids":["https://orcid.org/0000-0002-8149-0157"]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]},{"id":"https://openalex.org/I4405269449","display_name":"Wake Forest University Health Sciences","ror":"https://ror.org/046r1r105","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]},{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suraj Rajendran","raw_affiliation_strings":["Wake Forest University School of Medicine, Winston Salem, NC","Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wake Forest University School of Medicine, Winston Salem, NC","institution_ids":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]},{"raw_affiliation_string":"Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023480695","display_name":"\u00dcmit Topalo\u011dlu","orcid":"https://orcid.org/0000-0002-3241-8773","observed_orcids":["https://orcid.org/0000-0002-3241-8773"]},"institutions":[{"id":"https://openalex.org/I4405269449","display_name":"Wake Forest University Health Sciences","ror":"https://ror.org/046r1r105","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]},{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Umit Topaloglu","raw_affiliation_strings":["Wake Forest University School of Medicine, Winston Salem, NC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wake Forest University School of Medicine, Winston Salem, NC","institution_ids":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6755,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.72388415,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":"2020","issue":null,"first_page":"507","last_page":"516"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9535999894142151,"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":0.9535999894142151,"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/T10060","display_name":"Smoking Behavior and Cessation","score":0.013100000098347664,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.00839999970048666,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/tobacco-dependence","display_name":"tobacco dependence","score":0.7089999914169312},{"id":"https://openalex.org/keywords/clinical-progress-notes","display_name":"clinical progress notes","score":0.984000027179718},{"id":"https://openalex.org/keywords/electronic-health-records","display_name":"electronic health records","score":0.8320000171661377},{"id":"https://openalex.org/keywords/clinical-text-classification","display_name":"clinical text classification","score":0.7689999938011169},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8080000281333923},{"id":"https://openalex.org/keywords/bidirectional-lstm","display_name":"bidirectional LSTM","score":0.9269999861717224},{"id":"https://openalex.org/keywords/word-embeddings","display_name":"word embeddings","score":0.7929999828338623},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"multi-class classification","score":0.4909999966621399},{"id":"https://openalex.org/keywords/tobacco-related-mortality","display_name":"smoking-related mortality","score":0.42399999499320984}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8193626403808594},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6796842813491821},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6750110387802124},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6722774505615234},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.593234658241272},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5837454795837402},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.5309015512466431},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5189411640167236},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.48642921447753906},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.4824548363685608},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.4655763506889343},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4652218818664551},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.44061946868896484},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.42928871512413025},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.42119723558425903},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.20899221301078796},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.08348625898361206},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:pubmedcentral.nih.gov:7233082","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7233082","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 Jt Summits Transl Sci Proc","raw_type":"Text"},{"id":"pmid:32477672","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32477672","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 Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","raw_type":"Journal Article"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:7233082","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7233082","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 Jt Summits Transl Sci Proc","raw_type":"Text"},"sustainable_development_goals":[{"score":0.9537000060081482,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1849232265","display_name":"Wake Forest Clinical and Translational Science Award","funder_award_id":"UL1TR001420","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G9000328302","display_name":"Cancer Center Support Grant","funder_award_id":"P30CA012197","funder_id":"https://openalex.org/F4320337351","funder_display_name":"National Cancer Institute"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337351","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1177723","https://openalex.org/W1902025218","https://openalex.org/W1991092488","https://openalex.org/W2024862171","https://openalex.org/W2041789624","https://openalex.org/W2071478164","https://openalex.org/W2095444228","https://openalex.org/W2102224244","https://openalex.org/W2106107776","https://openalex.org/W2114584591","https://openalex.org/W2117230297","https://openalex.org/W2121399461","https://openalex.org/W2146179090","https://openalex.org/W2239231358","https://openalex.org/W2264517602","https://openalex.org/W2583686848","https://openalex.org/W2786776412","https://openalex.org/W2910777627","https://openalex.org/W2964006392"],"related_works":["https://openalex.org/W4397013501","https://openalex.org/W2799607375","https://openalex.org/W2541182945","https://openalex.org/W3044846437","https://openalex.org/W4416678760","https://openalex.org/W2322191948","https://openalex.org/W4245017556","https://openalex.org/W2786095215","https://openalex.org/W2795106452","https://openalex.org/W3199397177"],"abstract_inverted_index":{"Half":[0],"a":[1,71,73,77,87,91,128],"million":[2],"people":[3],"die":[4],"every":[5],"year":[6],"from":[7,46],"smoking-related":[8],"issues":[9],"across":[10],"the":[11,34,60,107,124,133],"United":[12],"States.":[13],"It":[14],"is":[15],"essential":[16],"to":[17,25,40,64,104],"identify":[18],"individuals":[19],"who":[20],"are":[21],"tobacco-dependent":[22],"in":[23],"order":[24],"implement":[26],"preventive":[27],"measures.":[28],"In":[29],"this":[30],"study,":[31],"we":[32],"investigate":[33],"effectiveness":[35],"of":[36,44,81],"deep":[37,68],"learning":[38,99],"models":[39,83,100],"extract":[41],"smoking":[42],"status":[43],"patients":[45],"clinical":[47],"progress":[48,61],"notes.":[49],"A":[50],"Natural":[51],"Language":[52],"Processing":[53],"(NLP)":[54],"Pipeline":[55],"was":[56,84],"built":[57],"that":[58,123],"cleans":[59],"notes":[62],"prior":[63],"processing":[65],"by":[66],"three":[67],"neural":[69,108],"networks:":[70],"CNN,":[72],"unidirectional":[74],"LSTM,":[75],"and":[76,116,138],"bidirectional":[78],"LSTM.":[79],"Each":[80,110],"these":[82],"trained":[85,89],"with":[86,127],"pre-":[88],"or":[90],"post-trained":[92],"word":[93],"embedding":[94,130],"layer.":[95],"Three":[96],"traditional":[97],"machine":[98],"were":[101],"also":[102],"employed":[103],"compare":[105],"against":[106],"networks.":[109],"model":[111,126],"has":[112],"generated":[113],"both":[114,136],"binary":[115,137],"multi-class":[117],"label":[118,141],"classification.":[119,142],"Our":[120],"results":[121],"showed":[122],"CNN":[125],"pre-trained":[129],"layer":[131],"performed":[132],"best":[134],"for":[135],"multi-":[139],"class":[140]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2020-06-05T00:00:00"}
