{"id":"https://openalex.org/W4205934771","doi":"https://doi.org/10.1109/bibm52615.2021.9669766","title":"Phenonizer: A fine-grained phenotypic named entity recognizer for Chinese clinical texts","display_name":"Phenonizer: A fine-grained phenotypic named entity recognizer for Chinese clinical texts","publication_year":2021,"publication_date":"2021-12-09","ids":{"openalex":"https://openalex.org/W4205934771","doi":"https://doi.org/10.1109/bibm52615.2021.9669766"},"language":"en","primary_location":{"id":"doi:10.1109/bibm52615.2021.9669766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669766","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5014295535","display_name":"Qunsheng Zou","orcid":"https://orcid.org/0000-0003-2604-2682"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qunsheng Zou","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051184139","display_name":"Kuo Yang","orcid":"https://orcid.org/0000-0003-0736-4512"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kuo Yang","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101534582","display_name":"Kai Chang","orcid":"https://orcid.org/0000-0002-4677-2466"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Chang","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100363169","display_name":"Xiaoping Zhang","orcid":"https://orcid.org/0000-0003-0995-4989"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoping Zhang","raw_affiliation_strings":["Data Centre of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Centre of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100369719","display_name":"Xiaodong Li","orcid":"https://orcid.org/0000-0003-0346-1526"},"institutions":[{"id":"https://openalex.org/I4210099122","display_name":"Hubei Provincial Hospital of Traditional Chinese Medicine","ror":"https://ror.org/00xabh388","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210099122"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Li","raw_affiliation_strings":["Hubei Provincial Academy of Traditional Chinese Medicine, Institute of Liver Disease, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei Provincial Academy of Traditional Chinese Medicine, Institute of Liver Disease, Wuhan, China","institution_ids":["https://openalex.org/I4210099122"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052838340","display_name":"Xuezhong Zhou","orcid":"https://orcid.org/0000-0002-4713-3594"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuezhong Zhou","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3963","last_page":"3970"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.998199999332428,"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":0.9973000288009644,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7940810322761536},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5951396226882935},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.55185866355896},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5328282117843628},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.48188096284866333},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.4414265751838684},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.36937639117240906},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3217621147632599}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7940810322761536},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5951396226882935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.55185866355896},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5328282117843628},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.48188096284866333},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.4414265751838684},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.36937639117240906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3217621147632599},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm52615.2021.9669766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669766","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320323485","display_name":"China Academy of Chinese Medical Sciences","ror":"https://ror.org/042pgcv68"},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W103980920","https://openalex.org/W2002514548","https://openalex.org/W2035703356","https://openalex.org/W2061848042","https://openalex.org/W2063753566","https://openalex.org/W2064362294","https://openalex.org/W2064675550","https://openalex.org/W2079735306","https://openalex.org/W2107435951","https://openalex.org/W2121244856","https://openalex.org/W2123512824","https://openalex.org/W2128535227","https://openalex.org/W2134429551","https://openalex.org/W2141869602","https://openalex.org/W2144578941","https://openalex.org/W2157331557","https://openalex.org/W2159636537","https://openalex.org/W2190421341","https://openalex.org/W2250539671","https://openalex.org/W2398489001","https://openalex.org/W2613831280","https://openalex.org/W2734608416","https://openalex.org/W2746134234","https://openalex.org/W2896457183","https://openalex.org/W2911489562","https://openalex.org/W2912971066","https://openalex.org/W2962739339","https://openalex.org/W2962904552","https://openalex.org/W2963339489","https://openalex.org/W2974256357","https://openalex.org/W2982424689","https://openalex.org/W2993873509","https://openalex.org/W3009799538","https://openalex.org/W3009951436","https://openalex.org/W3010276220","https://openalex.org/W3013616922","https://openalex.org/W3015631092","https://openalex.org/W4294170691","https://openalex.org/W6678414869","https://openalex.org/W6682691769","https://openalex.org/W6712207982","https://openalex.org/W6755207826","https://openalex.org/W6758293299"],"related_works":["https://openalex.org/W2980729574","https://openalex.org/W1560851690","https://openalex.org/W3092047717","https://openalex.org/W3110772647","https://openalex.org/W2770162183","https://openalex.org/W4390881630","https://openalex.org/W2971810784","https://openalex.org/W4312858192","https://openalex.org/W3107535086","https://openalex.org/W4389401521"],"abstract_inverted_index":{"Biomedical":[0],"named":[1,129],"entity":[2,130],"recognition":[3],"from":[4,156,227],"clinical":[5,12,36,53,100,114,122,167,229],"texts":[6,230],"is":[7,160],"a":[8,119,127,204,219],"fundamental":[9],"task":[10],"for":[11,50],"data":[13,40,108],"analysis":[14,109],"due":[15],"to":[16,83,180,222],"the":[17,52,67,84,106,136,175,190,198],"availability":[18],"of":[19,22,55,72,75,88,150,192,201,207],"large":[20],"volume":[21],"electronic":[23],"medical":[24,44],"record":[25],"data,":[26,158],"which":[27,46],"are":[28],"mostly":[29,64],"in":[30,34,57,78,99],"free":[31],"text":[32,39,80],"format,":[33],"real-world":[35],"settings.":[37],"Clinical":[38],"incorporates":[41],"significant":[42],"phenotypic":[43,76,89,128],"entities,":[45],"could":[47,103],"be":[48],"used":[49],"profiling":[51],"characteristics":[54],"patients":[56],"specific":[58],"disease":[59],"conditions.":[60],"However,":[61],"general":[62],"approaches":[63],"rely":[65],"on":[66,135,146],"coarse-grained":[68],"annotations":[69],"(e.g.":[70,91],"mentions":[71],"symptom":[73,225],"terms)":[74],"entities":[77,90],"benchmark":[79,215],"dataset.":[81],"Owing":[82],"numerous":[85],"negation":[86],"expressions":[87],"\u201cno":[92,94,97],"fever\u201d,":[93],"cough\u201d":[95],"and":[96,185,188],"hypertension\u201d)":[98],"texts,":[101],"this":[102],"not":[104],"feed":[105],"subsequent":[107],"process":[110],"with":[111,148,212,231],"well-prepared":[112],"structured":[113],"data.":[115],"Thus,":[116],"we":[117,125,196],"constructed":[118],"fine-grained":[120,176,213],"Chinese":[121,228],"corpus.":[123],"Thereafter,":[124],"proposed":[126],"recognizer":[131],"(Phenonizer).":[132],"The":[133],"results":[134],"test":[137],"set":[138],"show":[139],"that":[140,162],"Phenonizer":[141],"outperform":[142],"those":[143],"methods":[144,179],"based":[145],"Word2Vec":[147],"Fl-score":[149],"0.896.":[151],"By":[152],"comparing":[153],"character":[154,163],"embeddings":[155,164],"different":[157],"it":[159],"found":[161],"trained":[165],"by":[166,172],"corpora":[168],"can":[169],"improve":[170],"F-score":[171],"0.0103.":[173],"Furthermore,":[174],"dataset":[177],"enables":[178],"distinguish":[181],"between":[182],"negated":[183,193],"symptoms":[184],"presented":[186],"symptoms,":[187],"avoids":[189],"interference":[191],"symptoms.":[194],"Finally,":[195],"tested":[197],"generalization":[199],"performance":[200],"Phenonier,":[202],"achieving":[203],"superior":[205],"F1-score":[206],"0.8389.":[208],"In":[209],"summary,":[210],"together":[211],"annotated":[214],"dataset,":[216],"Phenonier":[217],"proposes":[218],"feasible":[220],"approach":[221],"effectively":[223],"extract":[224],"information":[226],"acceptable":[232],"performance.":[233]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
