{"id":"https://openalex.org/W4392981937","doi":"https://doi.org/10.1109/icaiic60209.2024.10463358","title":"A Hybrid Method for Clinical Text Classification Based on Confident Predictions and Regular Expressions","display_name":"A Hybrid Method for Clinical Text Classification Based on Confident Predictions and Regular Expressions","publication_year":2024,"publication_date":"2024-02-19","ids":{"openalex":"https://openalex.org/W4392981937","doi":"https://doi.org/10.1109/icaiic60209.2024.10463358"},"language":"en","primary_location":{"id":"doi:10.1109/icaiic60209.2024.10463358","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icaiic60209.2024.10463358","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","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/A5084370844","display_name":"Christopher A. Flores","orcid":"https://orcid.org/0000-0003-0994-5919"},"institutions":[{"id":"https://openalex.org/I4210144665","display_name":"University of O'Higgins","ror":"https://ror.org/044cse639","country_code":"CL","type":"education","lineage":["https://openalex.org/I4210144665"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Christopher A. Flores","raw_affiliation_strings":["Institute of Engineering Sciences, Universidad de O&#x0027;Higgins,Rancagua,Chile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Engineering Sciences, Universidad de O&#x0027;Higgins,Rancagua,Chile","institution_ids":["https://openalex.org/I4210144665"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086929271","display_name":"Rodrigo Verschae","orcid":"https://orcid.org/0000-0002-1661-3309"},"institutions":[{"id":"https://openalex.org/I4210144665","display_name":"University of O'Higgins","ror":"https://ror.org/044cse639","country_code":"CL","type":"education","lineage":["https://openalex.org/I4210144665"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Rodrigo Verschae","raw_affiliation_strings":["Institute of Engineering Sciences, Universidad de O&#x0027;Higgins,Rancagua,Chile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Engineering Sciences, Universidad de O&#x0027;Higgins,Rancagua,Chile","institution_ids":["https://openalex.org/I4210144665"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210144665"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02736025,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":null,"first_page":"064","last_page":"069"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9275000095367432,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9275000095367432,"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":0.6678831577301025},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4858998656272888},{"id":"https://openalex.org/keywords/regular-expression","display_name":"Regular expression","score":0.48305970430374146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44876596331596375},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.17761746048927307}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6678831577301025},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4858998656272888},{"id":"https://openalex.org/C121329065","wikidata":"https://www.wikidata.org/wiki/Q185612","display_name":"Regular expression","level":2,"score":0.48305970430374146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44876596331596375},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.17761746048927307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic60209.2024.10463358","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icaiic60209.2024.10463358","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1775135849","https://openalex.org/W2904029397","https://openalex.org/W2999309192","https://openalex.org/W3004838227","https://openalex.org/W3084965078","https://openalex.org/W3111112601","https://openalex.org/W3135832423","https://openalex.org/W3153077810","https://openalex.org/W4226184066","https://openalex.org/W4226418765","https://openalex.org/W4294974824","https://openalex.org/W4315641706","https://openalex.org/W4362719724","https://openalex.org/W4364355697","https://openalex.org/W4387331153","https://openalex.org/W4390568664","https://openalex.org/W6617145748","https://openalex.org/W6755207826"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2563334590","https://openalex.org/W2358668433","https://openalex.org/W3094387502","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2922478741","https://openalex.org/W2382290278","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Supervised":[0],"algorithms":[1,167,202],"allow":[2],"clinical":[3,28,90,100,190],"texts":[4,101,191],"to":[5,24,52,55,98,174,184],"be":[6,20,25,63],"automatically":[7,64],"organized":[8],"based":[9],"on":[10,127,160,168],"their":[11],"content.":[12],"In":[13,37],"this":[14,38],"sense,":[15],"supervised":[16,84,107,133,166,201],"algorithm":[17,85,108],"predictions":[18,81,104,198],"must":[19],"accurate":[21],"and":[22,86,129,145],"confident":[23,80,111],"used":[26,122],"in":[27,34,112,194],"practice,":[29],"considering":[30],"the":[31,35,78,103,157,163,179,197,200],"complex":[32,57],"patterns":[33,58],"texts.":[36],"aspect,":[39],"sequences":[40],"of":[41,82,105,114,165,181,189,199],"character":[42],"strings":[43],"known":[44],"as":[45,192],"regular":[46,87,96,186],"expressions":[47,88,97,187],"offer":[48],"an":[49],"alternative":[50],"closer":[51],"natural":[53],"language":[54],"represent":[56],"from":[59,149],"texts,":[60],"which":[61],"can":[62],"generated":[65],"using":[66],"sequence":[67],"alignment":[68],"algorithms.":[69],"This":[70],"paper":[71],"proposes":[72],"a":[73,83,106],"hybrid":[74],"method":[75,94,183],"that":[76,156],"combines":[77],"most":[79],"for":[89],"text":[91],"classification.":[92],"Our":[93],"uses":[95],"classify":[99],"when":[102,196],"are":[109],"not":[110,204],"terms":[113],"predictive":[115],"probability.":[116],"To":[117],"evaluate":[118],"our":[119,182],"method,":[120,159],"we":[121,177],"three":[123],"datasets":[124],"with":[125],"information":[126],"smoking":[128],"obesity":[130],"status":[131],"across":[132],"algorithms:":[134],"Support":[135],"Vector":[136],"Machine":[137],"(SVM),":[138],"Random":[139],"Forest":[140],"(RF),":[141],"Naive":[142],"Bayes":[143],"(NB),":[144],"Bidirectional":[146],"Encoder":[147],"Representations":[148],"Transformers":[150],"(BERT).":[151],"The":[152],"classification":[153],"results":[154],"indicate":[155],"proposed":[158],"average,":[161],"improved":[162],"performance":[164,170],"all":[169],"metrics":[171],"by":[172],"up":[173],"5%.":[175],"Thus,":[176],"demonstrated":[178],"ability":[180],"generate":[185],"representative":[188],"support":[193],"cases":[195],"were":[203],"confident.":[205]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
