{"id":"https://openalex.org/W2064123570","doi":"https://doi.org/10.1109/bigdata.congress.2014.101","title":"Tactical Clinical Text Mining for Improved Patient Characterization","display_name":"Tactical Clinical Text Mining for Improved Patient Characterization","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2064123570","doi":"https://doi.org/10.1109/bigdata.congress.2014.101","mag":"2064123570"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.congress.2014.101","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.congress.2014.101","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Congress on Big Data","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5070393738","display_name":"Heather Champion","orcid":"https://orcid.org/0000-0001-7304-2592"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heather Champion","raw_affiliation_strings":["IMT, Winnipeg, Canada","IMT, Winnipeg, MB, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IMT, Winnipeg, Canada","institution_ids":[]},{"raw_affiliation_string":"IMT, Winnipeg, MB, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110190505","display_name":"Nick J. Pizzi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nick Pizzi","raw_affiliation_strings":["IMT, Winnipeg, Canada","IMT, Winnipeg, MB, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IMT, Winnipeg, Canada","institution_ids":[]},{"raw_affiliation_string":"IMT, Winnipeg, MB, Canada","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063994342","display_name":"Raja Krishnamoorthy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raja Krishnamoorthy","raw_affiliation_strings":["IMT, Winnipeg, Canada","IMT, Winnipeg, MB, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IMT, Winnipeg, Canada","institution_ids":[]},{"raw_affiliation_string":"IMT, Winnipeg, MB, Canada","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3039,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.91733639,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"81","issue":null,"first_page":"683","last_page":"690"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9997000098228455,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9973000288009644,"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/T10028","display_name":"Topic Modeling","score":0.988099992275238,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7482791543006897},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.6102345585823059},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6098425388336182},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.604281485080719},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5727893114089966},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.5609378814697266},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.5485348701477051},{"id":"https://openalex.org/keywords/medical-prescription","display_name":"Medical prescription","score":0.46387967467308044},{"id":"https://openalex.org/keywords/text-mining","display_name":"Text mining","score":0.4527457356452942},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4469541311264038},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.44256240129470825},{"id":"https://openalex.org/keywords/noisy-text-analytics","display_name":"Noisy text analytics","score":0.4105711877346039},{"id":"https://openalex.org/keywords/biomedical-text-mining","display_name":"Biomedical text mining","score":0.4104555547237396},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4103281795978546},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4090580344200134},{"id":"https://openalex.org/keywords/text-graph","display_name":"Text graph","score":0.3764914870262146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3432677686214447},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.16402104496955872},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.14376232028007507}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7482791543006897},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.6102345585823059},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6098425388336182},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.604281485080719},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5727893114089966},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.5609378814697266},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.5485348701477051},{"id":"https://openalex.org/C2426938","wikidata":"https://www.wikidata.org/wiki/Q3355478","display_name":"Medical prescription","level":2,"score":0.46387967467308044},{"id":"https://openalex.org/C71472368","wikidata":"https://www.wikidata.org/wiki/Q676880","display_name":"Text mining","level":2,"score":0.4527457356452942},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4469541311264038},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.44256240129470825},{"id":"https://openalex.org/C151375590","wikidata":"https://www.wikidata.org/wiki/Q17147076","display_name":"Noisy text analytics","level":4,"score":0.4105711877346039},{"id":"https://openalex.org/C165141518","wikidata":"https://www.wikidata.org/wiki/Q4915126","display_name":"Biomedical text mining","level":3,"score":0.4104555547237396},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4103281795978546},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4090580344200134},{"id":"https://openalex.org/C66945725","wikidata":"https://www.wikidata.org/wiki/Q18388823","display_name":"Text graph","level":3,"score":0.3764914870262146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3432677686214447},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.16402104496955872},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.14376232028007507},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.congress.2014.101","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.congress.2014.101","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Congress on Big Data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7900000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W79139011","https://openalex.org/W1521396122","https://openalex.org/W1755473412","https://openalex.org/W1897351414","https://openalex.org/W1934602670","https://openalex.org/W1953828586","https://openalex.org/W2018560257","https://openalex.org/W2057350502","https://openalex.org/W2074509153","https://openalex.org/W2098720114","https://openalex.org/W2110367654","https://openalex.org/W2114388055","https://openalex.org/W2139865360","https://openalex.org/W2146089916","https://openalex.org/W2174118363","https://openalex.org/W3121289605","https://openalex.org/W6631239430","https://openalex.org/W6637677970","https://openalex.org/W6669153384","https://openalex.org/W6674867267","https://openalex.org/W6685442411","https://openalex.org/W6697227762"],"related_works":["https://openalex.org/W2770471982","https://openalex.org/W2770474375","https://openalex.org/W2152349655","https://openalex.org/W4384067529","https://openalex.org/W2011580521","https://openalex.org/W2372183225","https://openalex.org/W2389119968","https://openalex.org/W1625494842","https://openalex.org/W2365299969","https://openalex.org/W2475935882"],"abstract_inverted_index":{"Clinical":[0,38,136],"sources":[1],"of":[2,15,27,119,164,184,201],"information":[3,78],"are":[4,51,64],"markedly":[5],"increasing":[6],"in":[7,20,30,35,79],"both":[8],"volume":[9,116],"and":[10,47,57,75,117,172,192],"variety.":[11],"A":[12],"significant":[13],"portion":[14],"the":[16,21,105,115,139,146],"valuable":[17],"data":[18],"resides":[19],"unstructured":[22],"or":[23,33],"semi-structured":[24],"clinical":[25,98,120,128,178,194],"text":[26,152,179,195],"documents":[28,39],"stored":[29],"disparate":[31],"repositories":[32],"embedded":[34],"HL7":[36],"messages.":[37],"such":[40],"as":[41,159,161],"discharge":[42],"summaries,":[43],"prescriptions,":[44],"lab":[45],"reports,":[46],"free-form":[48,101],"physician":[49],"notes":[50],"filled":[52],"with":[53],"abbreviations,":[54],"acronyms,":[55],"misspellings,":[56],"ungrammatical":[58],"phrases.":[59],"However,":[60],"synoptic":[61],"reporting":[62],"methods":[63],"restrictive":[65],"for":[66,108],"health":[67],"care":[68],"practitioners":[69],"who":[70],"wish":[71],"to":[72,96,155,167],"express":[73],"critical":[74],"comprehensive":[76],"patient":[77],"electronic":[80],"medical":[81],"records.":[82],"Furthermore,":[83],"they":[84],"have":[85,123,149],"been":[86],"superseded":[87],"by":[88],"systems":[89],"that":[90,112],"use":[91],"natural":[92],"language":[93],"processing":[94],"(NLP)":[95],"extract":[97],"concepts":[99],"from":[100],"text.":[102],"To":[103],"address":[104],"growing":[106],"need":[107],"efficient":[109,191],"NLP":[110],"solutions":[111],"can":[113],"handle":[114],"variety":[118],"text,":[121],"we":[122,148],"developed":[124],"an":[125,176],"optimized":[126],"rules-based":[127],"concept":[129],"extractor":[130],"called":[131],"TRACE":[132],"(Tactical":[133],"Rules-based":[134],"AQL":[135],"Extractor)":[137],"using":[138],"Annotation":[140],"Query":[141],"Language":[142],"(AQL).":[143],"We":[144,187],"present":[145],"experience":[147],"gained":[150],"applying":[151],"mining":[153,196],"tools":[154],"this":[156],"challenging":[157],"domain,":[158],"well":[160],"a":[162,182],"comparison":[163],"our":[165,202],"solution":[166],"cTAKES":[168],"(clinical":[169],"Text":[170],"Analysis":[171],"Knowledge":[173],"Extraction":[174],"System),":[175],"open-source":[177],"miner,":[180],"on":[181],"set":[183],"prescription":[185],"documents.":[186],"also":[188],"describe":[189],"how":[190],"scalable":[193],"techniques":[197],"will":[198],"improve":[199],"several":[200],"company's":[203],"offerings.":[204]},"counts_by_year":[{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
