{"id":"https://openalex.org/W2772760183","doi":"https://doi.org/10.1109/bibm.2017.8217839","title":"Evaluating automatic methods to extract patients' supplement use from clinical reports","display_name":"Evaluating automatic methods to extract patients' supplement use from clinical reports","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2772760183","doi":"https://doi.org/10.1109/bibm.2017.8217839","mag":"2772760183","pmid":"https://pubmed.ncbi.nlm.nih.gov/29308296"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2017.8217839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2017.8217839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062761294","display_name":"Yadan Fan","orcid":"https://orcid.org/0000-0002-7797-815X"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yadan Fan","raw_affiliation_strings":["Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056691990","display_name":"He Lu","orcid":"https://orcid.org/0000-0001-6501-5447"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lu He","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100421963","display_name":"Rui Zhang","orcid":"https://orcid.org/0000-0001-8258-3585"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Zhang","raw_affiliation_strings":["Institute for Health Informatics, and College of Pharmacy, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Health Informatics, and College of Pharmacy, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":0.2744,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.56978013,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2017","issue":null,"first_page":"1258","last_page":"1261"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9916999936103821,"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":0.9916999936103821,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9857000112533569,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.9603000283241272,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"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/classifier","display_name":"Classifier (UML)","score":0.7100221514701843},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5928003787994385},{"id":"https://openalex.org/keywords/dietary-supplement","display_name":"Dietary supplement","score":0.5919508337974548},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.55885910987854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5581724643707275},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.373102605342865}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7100221514701843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5928003787994385},{"id":"https://openalex.org/C3018363719","wikidata":"https://www.wikidata.org/wiki/Q645858","display_name":"Dietary supplement","level":2,"score":0.5919508337974548},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.55885910987854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5581724643707275},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.373102605342865},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C31903555","wikidata":"https://www.wikidata.org/wiki/Q1637030","display_name":"Food science","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/bibm.2017.8217839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2017.8217839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},{"id":"pmid:29308296","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29308296","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,"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":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","raw_type":"Journal Article"},{"id":"pmh:oai:europepmc.org:4666171","is_oa":false,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5751954","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,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2635792716","display_name":null,"funder_award_id":"R01AT009457","funder_id":"https://openalex.org/F4320337593","funder_display_name":"National Center for Complementary and Integrative Health"},{"id":"https://openalex.org/G3134520348","display_name":null,"funder_award_id":"UL1 TR-000114","funder_id":"https://openalex.org/F4320337472","funder_display_name":"National Center for Advancing Translational Sciences"}],"funders":[{"id":"https://openalex.org/F4320337472","display_name":"National Center for Advancing Translational Sciences","ror":"https://ror.org/04pw6fb54"},{"id":"https://openalex.org/F4320337593","display_name":"National Center for Complementary and Integrative Health","ror":"https://ror.org/00190t495"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W180510140","https://openalex.org/W1541954861","https://openalex.org/W2004910511","https://openalex.org/W2114668172","https://openalex.org/W2166975295","https://openalex.org/W2330664058","https://openalex.org/W2417240622","https://openalex.org/W2572809958","https://openalex.org/W2786152859","https://openalex.org/W6607434451"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3209574120","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4210805261"],"abstract_inverted_index":{"The":[0,139],"widespread":[1],"prevalence":[2],"of":[3,23,46,51,59,80,122],"dietary":[4,27,47],"supplements":[5,48,81],"has":[6],"drawn":[7],"extensive":[8],"attention":[9],"due":[10],"to":[11,74,96,145,159],"the":[12,43,52,56,60,77,97,103,106,116,132,136],"safety":[13,40,62,161],"and":[14,70,91,126,155],"efficacy":[15],"issue.":[16],"Clinical":[17],"notes":[18,151],"document":[19],"a":[20,32,110],"great":[21],"amount":[22],"detailed":[24],"information":[25,148],"on":[26,38,102,162],"supplement":[28,39,61,147,163],"usage,":[29],"thus":[30],"providing":[31],"rich":[33],"source":[34],"for":[35,55,152],"clinical":[36,150,156],"research":[37,154],"surveillance.":[41,63],"Identification":[42],"use":[44,78],"status":[45,79,121],"is":[49],"one":[50],"initial":[53],"steps":[54],"ultimate":[57],"goal":[58],"In":[64,94],"this":[65],"study,":[66],"we":[67],"built":[68],"rule-based":[69,107,137],"machine":[71,98],"learning-based":[72],"classifiers":[73],"automatically":[75],"classify":[76],"into":[82],"four":[83],"categories:":[84],"Continuing":[85],"(C),":[86],"Discontinued":[87],"(D),":[88],"Started":[89],"(S),":[90],"Unclassified":[92],"(U).":[93],"comparison":[95],"learning":[99],"classifier":[100,108,140],"trained":[101],"same":[104],"datasets,":[105],"showed":[109],"better":[111],"performance":[112],"with":[113],"F-measure":[114],"in":[115],"C,":[117],"D,":[118],"S,":[119],"U":[120],"0.93,":[123],"0.98,":[124],"0.95,":[125],"0.83,":[127],"respectively.":[128],"We":[129],"further":[130],"analyzed":[131],"errors":[133],"generated":[134],"by":[135],"classifier.":[138],"can":[141],"be":[142],"potentially":[143],"applied":[144],"extract":[146],"from":[149],"supporting":[153],"practice":[157],"related":[158],"patient":[160],"usage.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
