{"id":"https://openalex.org/W7160241662","doi":"https://doi.org/10.5753/sbsi.2026.248362","title":"Noise in Brazilian Clinical Anamnesis: An Empirical Study","display_name":"Noise in Brazilian Clinical Anamnesis: An Empirical Study","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160241662","doi":"https://doi.org/10.5753/sbsi.2026.248362"},"language":null,"primary_location":{"id":"doi:10.5753/sbsi.2026.248362","is_oa":true,"landing_page_url":"https://doi.org/10.5753/sbsi.2026.248362","pdf_url":"https://sol.sbc.org.br/index.php/sbsi/article/download/41334/41104","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anais do XXII Simp\u00f3sio Brasileiro de Sistemas de Informa\u00e7\u00e3o (SBSI 2026)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://sol.sbc.org.br/index.php/sbsi/article/download/41334/41104","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135347875","display_name":"Leandro A. Carvalho","orcid":null},"institutions":[{"id":"https://openalex.org/I2802300007","display_name":"United Food and Commercial Workers","ror":"https://ror.org/0165zav23","country_code":"US","type":"other","lineage":["https://openalex.org/I2802300007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leandro A. Carvalho","raw_affiliation_strings":["UFC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UFC","institution_ids":["https://openalex.org/I2802300007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135357166","display_name":"Thiago Mikael da Silva Oliveira","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thiago Q. Oliveira","raw_affiliation_strings":["IFCE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IFCE","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135311161","display_name":"Fl\u00e1vio R. C. Sousa","orcid":null},"institutions":[{"id":"https://openalex.org/I2802300007","display_name":"United Food and Commercial Workers","ror":"https://ror.org/0165zav23","country_code":"US","type":"other","lineage":["https://openalex.org/I2802300007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fl\u00e1vio R. C. Sousa","raw_affiliation_strings":["UFC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UFC","institution_ids":["https://openalex.org/I2802300007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124721130","display_name":"Jo\u00e3o B. F. Filho","orcid":null},"institutions":[{"id":"https://openalex.org/I2802300007","display_name":"United Food and Commercial Workers","ror":"https://ror.org/0165zav23","country_code":"US","type":"other","lineage":["https://openalex.org/I2802300007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jo\u00e3o B. F. Filho","raw_affiliation_strings":["UFC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UFC","institution_ids":["https://openalex.org/I2802300007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.60668265,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"402","last_page":"419"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.3075999915599823,"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":0.3075999915599823,"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/T10350","display_name":"Electronic Health Records Systems","score":0.22779999673366547,"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"}},{"id":"https://openalex.org/T12790","display_name":"Nursing Diagnosis and Documentation","score":0.121799997985363,"subfield":{"id":"https://openalex.org/subfields/2910","display_name":"Issues, ethics and legal aspects"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5516999959945679},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.49559998512268066},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.48339998722076416},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42329999804496765},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4163999855518341},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.3926999866962433},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.3716000020503998}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.698199987411499},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5516999959945679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5329999923706055},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.49559998512268066},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.48339998722076416},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.47540000081062317},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4537000060081482},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42329999804496765},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4163999855518341},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.3926999866962433},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.3716000020503998},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.36230000853538513},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.35280001163482666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3490000069141388},{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.30649998784065247},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C109747225","wikidata":"https://www.wikidata.org/wiki/Q815758","display_name":"Scarcity","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5753/sbsi.2026.248362","is_oa":true,"landing_page_url":"https://doi.org/10.5753/sbsi.2026.248362","pdf_url":"https://sol.sbc.org.br/index.php/sbsi/article/download/41334/41104","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anais do XXII Simp\u00f3sio Brasileiro de Sistemas de Informa\u00e7\u00e3o (SBSI 2026)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.5753/sbsi.2026.248362","is_oa":true,"landing_page_url":"https://doi.org/10.5753/sbsi.2026.248362","pdf_url":"https://sol.sbc.org.br/index.php/sbsi/article/download/41334/41104","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anais do XXII Simp\u00f3sio Brasileiro de Sistemas de Informa\u00e7\u00e3o (SBSI 2026)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7481688857078552,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7160241662.pdf","grobid_xml":"https://content.openalex.org/works/W7160241662.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W63042057","https://openalex.org/W177117832","https://openalex.org/W2156235098","https://openalex.org/W2168041406","https://openalex.org/W2238966896","https://openalex.org/W2396881363","https://openalex.org/W2508848934","https://openalex.org/W2625625371","https://openalex.org/W2757504960","https://openalex.org/W2805211535","https://openalex.org/W2966351171","https://openalex.org/W2970228048","https://openalex.org/W2997514453","https://openalex.org/W3003406981","https://openalex.org/W3038337155","https://openalex.org/W3198690080","https://openalex.org/W4226126382","https://openalex.org/W4291021452","https://openalex.org/W4291213652","https://openalex.org/W4295807725","https://openalex.org/W4313439128","https://openalex.org/W4399153297","https://openalex.org/W4408781676"],"related_works":[],"abstract_inverted_index":{"Research":[0,142],"Context:":[1],"The":[2,188],"lack":[3],"of":[4,11,69,75,124,134,186,190,193,202,210,221,236],"representative":[5],"data":[6,24,70,222,262],"can":[7,25,56,71,138],"limit":[8,72],"the":[9,67,73,119,122,132,215,218,247,261],"development":[10,74],"robust":[12],"clinical":[13,52,77,105,194,257],"Natural":[14],"Language":[15],"Processing":[16],"(NLP)":[17],"models,":[18,136],"as":[19,63],"models":[20,46],"trained":[21],"on":[22,28,49],"idealized":[23],"perform":[26],"poorly":[27],"noisy":[29,129,212,248],"real-world":[30,51],"Electronic":[31],"Health":[32],"Records":[33],"(EHRs).":[34],"Scientific":[35],"and/or":[36,82],"Practical":[37],"Problem:":[38],"A":[39,144,234],"performance":[40],"gap":[41],"exists":[42],"when":[43],"these":[44],"NLP":[45,135,271],"are":[47],"deployed":[48],"noisy,":[50],"text.":[53],"This":[54,84],"issue":[55],"be":[57,267],"found":[58,101],"in":[59,102,111,225],"less-resourced":[60],"languages,":[61],"such":[62],"Brazilian":[64,103,255],"Portuguese,":[65],"where":[66],"scarcity":[68],"effective":[76],"information":[78,126],"systems.":[79],"Proposed":[80],"Solution":[81],"Analysis:":[83],"study":[85,117],"addresses":[86],"this":[87,116,175,226],"challenge":[88],"by":[89,168,240],"presenting":[90],"a":[91,155,169,191,199,207],"systematic":[92],"approach":[93],"to":[94,149,158,173,231],"identify":[95,150],"and":[96,131,165,229,259],"quantify":[97],"textual":[98,151,203,237],"noise":[99,181],"patterns":[100],"Portuguese":[104,256],"narratives.":[106],"Related":[107],"IS":[108,232],"Theory:":[109],"Based":[110],"Task-Technology":[112],"Fit":[113],"(TTF)":[114],"Theory,":[115],"investigates":[118],"misalignment":[120],"between":[121],"task":[123],"reliable":[125],"extraction":[127],"from":[128],"EHRs":[130],"technology":[133],"which":[137],"presuppose":[139],"clean":[140],"data.":[141],"Method:":[143],"multi-stage":[145],"methodology":[146],"was":[147],"employed":[148],"noise.":[152],"Starting":[153],"with":[154],"classification":[156],"stage":[157],"flag":[159],"candidate":[160],"tokens":[161,213],"likely":[162],"representing":[163],"typos":[164],"abbreviations,":[166],"followed":[167],"lexicon-based":[170],"validation":[171],"executed":[172],"refine":[174],"selection,":[176],"ensuring":[177],"that":[178,244,265],"only":[179,198],"authentic":[180],"instances":[182],"were":[183],"selected.":[184],"Summary":[185],"Results:":[187],"analysis":[189],"dataset":[192],"anamneses":[195],"revealed":[196],"not":[197],"high":[200],"incidence":[201],"noise,":[204,238],"but":[205],"also":[206],"consistent":[208],"recurrence":[209],"specific":[211],"across":[214],"dataset,":[216],"demonstrating":[217],"widespread":[219],"nature":[220],"quality":[223,263],"issues":[224],"domain.":[227],"Contributions":[228],"Impact":[230],"area:":[233],"taxonomy":[235],"complemented":[239],"two":[241],"JSON":[242],"files":[243],"structurally":[245],"map":[246],"tokens,":[249],"establishing":[250],"an":[251],"empirical":[252],"benchmark":[253],"for":[254,269],"text":[258],"formalizing":[260],"challenges":[264],"must":[266],"overcome":[268],"successful":[270],"implementation.":[272]},"counts_by_year":[],"updated_date":"2026-06-17T06:14:20.161405","created_date":"2026-05-06T00:00:00"}
