{"id":"https://openalex.org/W4392011968","doi":"https://doi.org/10.48550/arxiv.2402.11177","title":"A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction","display_name":"A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction","publication_year":2024,"publication_date":"2024-02-17","ids":{"openalex":"https://openalex.org/W4392011968","doi":"https://doi.org/10.48550/arxiv.2402.11177"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2402.11177","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.11177","pdf_url":"https://arxiv.org/pdf/2402.11177","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2402.11177","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017881866","display_name":"Huaiyuan Ying","orcid":"https://orcid.org/0000-0001-7959-2093","observed_orcids":["https://orcid.org/0000-0001-7959-2093"]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying, Huaiyuan","raw_affiliation_strings":["Center for Statistical Science Tsinghua University Beijing , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Statistical Science Tsinghua University Beijing , China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040501126","display_name":"Sheng Yu","orcid":"https://orcid.org/0000-0002-6347-0507","observed_orcids":["https://orcid.org/0000-0002-6347-0507"]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu, Sheng","raw_affiliation_strings":["Center for Statistical Science Tsinghua University Beijing , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Statistical Science Tsinghua University Beijing , China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.6693999767303467,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.6693999767303467,"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":0.12770000100135803,"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/T10028","display_name":"Topic Modeling","score":0.09860000014305115,"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/electronic-health-records","display_name":"electronic health records","score":0.890999972820282},{"id":"https://openalex.org/keywords/clinical-information-extraction","display_name":"clinical information extraction","score":0.871999979019165},{"id":"https://openalex.org/keywords/question-answering","display_name":"question answering","score":0.9589999914169312},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.7639999985694885},{"id":"https://openalex.org/keywords/training-data-generation","display_name":"training data generation","score":0.8399999737739563},{"id":"https://openalex.org/keywords/few-shot-learning","display_name":"few-shot learning","score":0.6639999747276306},{"id":"https://openalex.org/keywords/zero-shot-learning","display_name":"zero-shot learning","score":0.9909999966621399},{"id":"https://openalex.org/keywords/polar-questions","display_name":"yes-no questions","score":0.8009999990463257},{"id":"https://openalex.org/keywords/chinese-text","display_name":"Chinese text","score":0.34599998593330383}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7818740010261536},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5558282136917114},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5271724462509155},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.47039613127708435},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.411784291267395},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4048348367214203},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3775465190410614},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.09124541282653809},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2402.11177","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.11177","pdf_url":"https://arxiv.org/pdf/2402.11177","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2402.11177","doi":"https://doi.org/10.48550/arxiv.2402.11177","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2402.11177","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2402.11177","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.11177","pdf_url":"https://arxiv.org/pdf/2402.11177","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392011968.pdf","grobid_xml":"https://content.openalex.org/works/W4392011968.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2384605597","https://openalex.org/W2387743295","https://openalex.org/W2115758952","https://openalex.org/W3082787378","https://openalex.org/W2136007095","https://openalex.org/W2366230879","https://openalex.org/W3208425359","https://openalex.org/W2349927912","https://openalex.org/W3159777597","https://openalex.org/W1984061923"],"abstract_inverted_index":{"Electronic":[0],"health":[1],"records":[2],"(EHRs)":[3],"hold":[4],"significant":[5],"value":[6],"for":[7,60,143],"research":[8],"and":[9,29,93,110,124,136],"applications.":[10],"As":[11],"a":[12,52,69],"new":[13],"way":[14],"of":[15,44,63,105,130],"information":[16,25,106],"extraction,":[17],"question":[18],"answering":[19],"(QA)":[20],"can":[21,112],"extract":[22],"more":[23,31],"flexible":[24],"than":[26],"conventional":[27],"methods":[28],"is":[30,39,140],"accessible":[32],"to":[33,72],"clinical":[34],"researchers,":[35],"but":[36],"its":[37],"progress":[38],"impeded":[40],"by":[41,76],"the":[42,128,137],"scarcity":[43],"annotated":[45],"data.":[46],"In":[47],"this":[48],"paper,":[49],"we":[50],"propose":[51],"novel":[53],"approach":[54],"that":[55,79],"automatically":[56],"generates":[57],"training":[58],"data":[59],"transfer":[61],"learning":[62],"QA":[64,86,98],"models.":[65],"Our":[66],"pipeline":[67],"incorporates":[68],"preprocessing":[70],"module":[71],"handle":[73,114],"challenges":[74],"posed":[75],"extraction":[77,107],"types":[78],"are":[80],"not":[81],"readily":[82],"compatible":[83],"with":[84,90],"extractive":[85],"frameworks,":[87],"including":[88],"cases":[89],"discontinuous":[91],"answers":[92],"many-to-one":[94],"relationships.":[95],"The":[96],"obtained":[97],"model":[99,139],"exhibits":[100],"excellent":[101],"performance":[102],"on":[103],"subtasks":[104],"in":[108,133],"EHRs,":[109],"it":[111],"effectively":[113],"few-shot":[115],"or":[116],"zero-shot":[117],"settings":[118],"involving":[119],"yes-no":[120],"questions.":[121],"Case":[122],"studies":[123,126],"ablation":[125],"demonstrate":[127],"necessity":[129],"each":[131],"component":[132],"our":[134],"design,":[135],"resulting":[138],"deemed":[141],"suitable":[142],"practical":[144],"use.":[145]},"counts_by_year":[],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2025-10-10T00:00:00"}