{"id":"https://openalex.org/W2400010002","doi":null,"title":"Identifying Contextual Information in Clinical Texts: A Study of Two Domains","display_name":"Identifying Contextual Information in Clinical Texts: A Study of Two Domains","publication_year":2011,"publication_date":"2011-11-01","ids":{"openalex":"https://openalex.org/W2400010002","mag":"2400010002"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.204.5017","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.204.5017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-714/ShortPaper01_Apostolova.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Emilia Apostolova","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emilia Apostolova","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022882291","display_name":"Noriko Tomuro","orcid":"https://orcid.org/0009-0009-8673-5078","observed_orcids":["https://orcid.org/0009-0009-8673-5078"]},"institutions":[{"id":"https://openalex.org/I118353179","display_name":"DePaul University","ror":"https://ror.org/04xtx5t16","country_code":"US","type":"education","lineage":["https://openalex.org/I118353179"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Noriko Tomuro","raw_affiliation_strings":["*DePaul University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"*DePaul University","institution_ids":["https://openalex.org/I118353179"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046764593","display_name":"Dina Demner\u2010Fushman","orcid":"https://orcid.org/0000-0002-4361-5799","observed_orcids":["https://orcid.org/0000-0002-4361-5799"]},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dina Demner-fushman","raw_affiliation_strings":["National Institutes of Health"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institutes of Health","institution_ids":["https://openalex.org/I1299303238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9945999979972839,"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"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9945999979972839,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.003700000001117587,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.00039999998989515007,"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/contextual-information","display_name":"contextual information","score":0.6729999780654907},{"id":"https://openalex.org/keywords/clinical-information-extraction","display_name":"clinical information extraction","score":0.8399999737739563},{"id":"https://openalex.org/keywords/negation-detection","display_name":"negation detection","score":0.6439999938011169},{"id":"https://openalex.org/keywords/annotated-training-data","display_name":"annotated training data","score":0.6010000109672546},{"id":"https://openalex.org/keywords/radiology-reports","display_name":"radiology reports","score":0.921999990940094},{"id":"https://openalex.org/keywords/discharge-summaries","display_name":"discharge summaries","score":0.9810000061988831},{"id":"https://openalex.org/keywords/progress-notes","display_name":"progress notes","score":0.9850000143051147},{"id":"https://openalex.org/keywords/rule-based-systems","display_name":"rule-based systems","score":0.8240000009536743},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.8920000195503235}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7650114297866821},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5639223456382751},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5575517416000366},{"id":"https://openalex.org/C40422974","wikidata":"https://www.wikidata.org/wiki/Q741248","display_name":"Assertion","level":2,"score":0.5388562083244324},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.5349551439285278},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.48883384466171265},{"id":"https://openalex.org/C2185349","wikidata":"https://www.wikidata.org/wiki/Q190558","display_name":"Negation","level":2,"score":0.4886205792427063},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.480163037776947},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4510082006454468},{"id":"https://openalex.org/C137981799","wikidata":"https://www.wikidata.org/wiki/Q1369184","display_name":"Reusability","level":3,"score":0.43714430928230286},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.37912559509277344},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11484083533287048},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.10353854298591614},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.204.5017","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.204.5017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-714/ShortPaper01_Apostolova.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5683000087738037,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W4508078","https://openalex.org/W182767159","https://openalex.org/W1550258693","https://openalex.org/W2043335066","https://openalex.org/W2114388055","https://openalex.org/W2139865360","https://openalex.org/W2153635508"],"related_works":["https://openalex.org/W1502835421","https://openalex.org/W2150944609","https://openalex.org/W2564408440","https://openalex.org/W7066690118","https://openalex.org/W4410818840","https://openalex.org/W6885168145","https://openalex.org/W2288946185","https://openalex.org/W7097348283","https://openalex.org/W2104752900","https://openalex.org/W3206589498"],"abstract_inverted_index":{"Contextual":[0],"information,":[1],"such":[2],"as":[3],"the":[4,29,41,55,79,142,155,172],"affirmation":[5],"or":[6],"negation":[7],"of":[8,32,43,58,88,127,174],"medical":[9],"problems,":[10],"is":[11],"key":[12],"in":[13],"Information":[14],"Extraction":[15],"(IE)":[16],"from":[17],"clinical":[18,27,45,60,169],"texts.":[19],"As":[20],"there":[21,115],"are":[22],"very":[23],"few":[24],"available":[25],"annotated":[26,62,162],"datasets,":[28],"practical":[30],"question":[31],"training":[33,85,163],"corpora":[34],"reusability":[35,57],"arises.":[36],"Clinical":[37],"IE":[38],"systems":[39],"face":[40],"challenge":[42],"disparate":[44],"sub-domain":[46],"narratives":[47],"typically":[48],"lacking":[49],"targeted":[50],"training/testing":[51],"data.":[52],"We":[53,103],"evaluated":[54,96],"crossdomain":[56],"a":[59,84,98,105,109,117,124,147],"dataset":[61,86,126,164],"with":[63,78,136],"contextual":[64,176],"information-medical":[65],"concepts":[66],"and":[67,91,108,112],"their":[68],"assertion":[69],"status":[70],"(present,":[71],"absent,":[72],"hypothetical,":[73],"possible,":[74],"conditional,":[75],"not":[76],"associated":[77],"patient).":[80],"Systems":[81],"developed":[82,104],"on":[83,97],"consisting":[87],"discharge":[89],"summaries":[90],"progress":[92],"notes":[93],"were":[94],"then":[95],"new":[99,125],"sub-domain-":[100],"radiology":[101,128],"reports.":[102,129],"machine":[106,143],"learning":[107,144],"rule-based":[110,131,156],"system":[111,132],"observed":[113],"that":[114,160],"was":[116],"minor":[118],"performance":[119,135,150],"drop":[120],"when":[121],"applied":[122],"to":[123],"The":[130],"exhibited":[133],"stable":[134],"no":[137],"statistically":[138,148],"significant":[139,149],"change.":[140],"While":[141],"approach":[145],"had":[146],"drop,":[151],"it":[152],"still":[153],"outperformed":[154],"system.":[157],"Results":[158],"suggest":[159],"an":[161],"could":[165],"be":[166],"reused":[167],"across":[168],"subdomains":[170],"for":[171],"task":[173],"identifying":[175],"information.":[177],"1":[178]},"counts_by_year":[],"updated_date":"2026-09-29T14:09:16.559479","created_date":"2016-06-24T00:00:00"}