{"id":"https://openalex.org/W3005126219","doi":"https://doi.org/10.1109/bibm47256.2019.8983098","title":"A General Fine-tuned Transfer Learning Model for Predicting Clinical Task Acrossing Diverse EHRs Datasets","display_name":"A General Fine-tuned Transfer Learning Model for Predicting Clinical Task Acrossing Diverse EHRs Datasets","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3005126219","doi":"https://doi.org/10.1109/bibm47256.2019.8983098","mag":"3005126219"},"language":"en","primary_location":{"id":"doi:10.1109/bibm47256.2019.8983098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm47256.2019.8983098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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/A5025554387","display_name":"Zhe Sun","orcid":"https://orcid.org/0000-0002-6531-0769"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Sun","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108008219","display_name":"Shaoliang Peng","orcid":"https://orcid.org/0000-0002-4647-2615"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoliang Peng","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101520782","display_name":"Yaning Yang","orcid":"https://orcid.org/0000-0003-2408-4176"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaning Yang","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401784","display_name":"Xiaoqi Wang","orcid":"https://orcid.org/0000-0002-5868-5044"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqi Wang","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100325881","display_name":"Fei Li","orcid":"https://orcid.org/0000-0003-1816-1761"},"institutions":[{"id":"https://openalex.org/I4210132047","display_name":"Beijing Radiation Center","ror":"https://ror.org/030t7nd77","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210132047"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Li","raw_affiliation_strings":["Beijing Institute of Radiation Medicine,Department of biotechnology,Beijing,China","Department of biotechnology, Beijing Institute of Radiation Medicine, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Radiation Medicine,Department of biotechnology,Beijing,China","institution_ids":["https://openalex.org/I4210132047"]},{"raw_affiliation_string":"Department of biotechnology, Beijing Institute of Radiation Medicine, Beijing, China","institution_ids":["https://openalex.org/I4210132047"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"490","last_page":"495"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998999834060669,"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":0.9998999834060669,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9843999743461609,"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/T10350","display_name":"Electronic Health Records Systems","score":0.972000002861023,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7804579734802246},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.7362188100814819},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6943502426147461},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6881338953971863},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6759116649627686},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.6574233174324036},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5920971035957336},{"id":"https://openalex.org/keywords/electronic-health-record","display_name":"Electronic health record","score":0.5626121163368225},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.44331303238868713},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37996283173561096},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.2856879234313965}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7804579734802246},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7362188100814819},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6943502426147461},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6881338953971863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6759116649627686},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.6574233174324036},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5920971035957336},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.5626121163368225},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.44331303238868713},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37996283173561096},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.2856879234313965},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm47256.2019.8983098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm47256.2019.8983098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1605960500","https://openalex.org/W1808652302","https://openalex.org/W1924770834","https://openalex.org/W2282887873","https://openalex.org/W2285597872","https://openalex.org/W2396881363","https://openalex.org/W2410450155","https://openalex.org/W2608683779","https://openalex.org/W2747048035","https://openalex.org/W2784499877","https://openalex.org/W2891400669","https://openalex.org/W3098949126","https://openalex.org/W3101973032","https://openalex.org/W4247943214","https://openalex.org/W4385245566","https://openalex.org/W6630988046","https://openalex.org/W6636248600","https://openalex.org/W6714112955","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W4312200629","https://openalex.org/W4382286161","https://openalex.org/W4318957922","https://openalex.org/W2960456850","https://openalex.org/W2946016983","https://openalex.org/W4380611590","https://openalex.org/W4318834068","https://openalex.org/W4317565044","https://openalex.org/W4312685930","https://openalex.org/W4322727400"],"abstract_inverted_index":{"Data":[0,53],"analysis":[1],"of":[2,66],"electronic":[3,26,67],"health":[4,27,68],"record":[5,28],"(EHRs)":[6],"system":[7],"using":[8],"machine":[9],"learning,":[10],"statistical":[11],"methods":[12],"can":[13,81,200],"predict":[14,209],"relevant":[15],"clinical":[16,32,86,114,210],"tasks.":[17,211],"However,":[18],"there":[19],"is":[20],"no":[21],"uniform":[22],"standard":[23],"for":[24],"current":[25],"systems,":[29],"and":[30,92,110,112,136,161],"the":[31,64,128,133,138,147,152,158,163,172,179,197],"outcome":[33],"prediction":[34,87],"models":[35,83,131,174],"trained":[36,127],"on":[37,45,97,132,166,178],"one":[38,113],"EHR":[39,47],"dataset":[40,135,160],"cannot":[41],"be":[42],"applied":[43],"well":[44],"other":[46],"datasets":[48,91],"from":[49],"different":[50,56,103],"medical":[51,57],"institutions.":[52],"differences":[54],"between":[55],"institutions":[58],"pose":[59],"a":[60,75],"huge":[61],"challenge":[62],"to":[63,84,120,157,175,208],"study":[65],"records.":[69],"In":[70],"this":[71],"study,":[72],"we":[73,126,145,170],"proposed":[74],"general":[76,198],"transfer":[77,192],"learning":[78,100,130],"strategy":[79,199],"which":[80,194],"enable":[82],"make":[85,176],"acrossing":[88,205],"diverse":[89],"EHRs":[90,206],"validated":[93],"its":[94],"strong":[95],"versatility":[96],"three":[98],"deep":[99,129],"models.":[101],"Two":[102],"intensive":[104],"care":[105],"units":[106],"(ICU)":[107],"databases":[108,207],"(MIMIC-III":[109],"eICU)":[111],"task":[115],"(in-hospital":[116],"mortality)":[117],"are":[118],"used":[119],"evaluate":[121],"our":[122],"method.":[123],"At":[124],"first,":[125],"source":[134],"saved":[137],"model":[139,150],"states":[140],"after":[141],"each":[142],"epoch.":[143],"Then,":[144],"selected":[146],"best":[148],"performing":[149],"as":[151],"pre-training":[153],"model,":[154],"transferred":[155],"it":[156],"target":[159,167,180],"fine-tuned":[162,173],"whole":[164],"network":[165],"dataset.":[168,181],"Finally,":[169],"use":[171],"predictions":[177,204],"Experiment":[182],"results":[183],"show":[184],"that":[185,196],"AUROC":[186],"score":[187],"increased":[188],"by":[189],"3%-20%":[190],"with":[191],"strategy,":[193],"indicated":[195],"provide":[201],"more":[202],"reliable":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
