{"id":"https://openalex.org/W2979680077","doi":"https://doi.org/10.1109/embc.2019.8857657","title":"Using machine learning models to classify stroke risk level based on national screening data","display_name":"Using machine learning models to classify stroke risk level based on national screening data","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2979680077","doi":"https://doi.org/10.1109/embc.2019.8857657","mag":"2979680077","pmid":"https://pubmed.ncbi.nlm.nih.gov/31946151"},"language":"en","primary_location":{"id":"doi:10.1109/embc.2019.8857657","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc.2019.8857657","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref","pubmed"],"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/A5019528447","display_name":"Xuemeng Li","orcid":null},"institutions":[{"id":"https://openalex.org/I2802541053","display_name":"Academy of Military Medical Sciences","ror":"https://ror.org/02bv3c993","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802541053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuemeng Li","raw_affiliation_strings":["Information Center, Academy of Military Medical Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Center, Academy of Military Medical Sciences, Beijing, China","institution_ids":["https://openalex.org/I2802541053"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019509578","display_name":"Di Bian","orcid":null},"institutions":[{"id":"https://openalex.org/I110440473","display_name":"Xi'an University of Science and Technology","ror":"https://ror.org/046fkpt18","country_code":"CN","type":"education","lineage":["https://openalex.org/I110440473"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Bian","raw_affiliation_strings":["School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, China","institution_ids":["https://openalex.org/I110440473"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100983366","display_name":"Jinghui Yu","orcid":"https://orcid.org/0009-0000-0446-3721"},"institutions":[{"id":"https://openalex.org/I2802541053","display_name":"Academy of Military Medical Sciences","ror":"https://ror.org/02bv3c993","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802541053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JingHui Yu","raw_affiliation_strings":["Information Center, Academy of Military Medical Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Center, Academy of Military Medical Sciences, Beijing, China","institution_ids":["https://openalex.org/I2802541053"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012136888","display_name":"Huajian Mao","orcid":"https://orcid.org/0000-0002-5609-6270"},"institutions":[{"id":"https://openalex.org/I2802541053","display_name":"Academy of Military Medical Sciences","ror":"https://ror.org/02bv3c993","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802541053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"HuaJian Mao","raw_affiliation_strings":["Information Center, Academy of Military Medical Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Center, Academy of Military Medical Sciences, Beijing, China","institution_ids":["https://openalex.org/I2802541053"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100331933","display_name":"Mei Li","orcid":"https://orcid.org/0000-0003-2313-7969"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mei Li","raw_affiliation_strings":["China Stroke Data Center, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Stroke Data Center, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102751782","display_name":"Dongsheng Zhao","orcid":"https://orcid.org/0000-0003-2616-8891"},"institutions":[{"id":"https://openalex.org/I2802541053","display_name":"Academy of Military Medical Sciences","ror":"https://ror.org/02bv3c993","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802541053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongsheng Zhao","raw_affiliation_strings":["Information Center, Academy of Military Medical Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Center, Academy of Military Medical Sciences, Beijing, China","institution_ids":["https://openalex.org/I2802541053"]}]}],"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":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2019","issue":null,"first_page":"1386","last_page":"1390"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9958999752998352,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9958999752998352,"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":0.9919999837875366,"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/T10227","display_name":"Acute Ischemic Stroke Management","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/stroke","display_name":"Stroke (engine)","score":0.6159346699714661},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.5888593196868896},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5590636730194092},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.47492465376853943},{"id":"https://openalex.org/keywords/psychological-intervention","display_name":"Psychological intervention","score":0.47379180788993835},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4396735727787018},{"id":"https://openalex.org/keywords/framingham-risk-score","display_name":"Framingham Risk Score","score":0.4183945059776306},{"id":"https://openalex.org/keywords/physical-therapy","display_name":"Physical therapy","score":0.37658485770225525},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3115011155605316},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30214694142341614},{"id":"https://openalex.org/keywords/environmental-health","display_name":"Environmental health","score":0.1922900676727295},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.11475872993469238}],"concepts":[{"id":"https://openalex.org/C2780645631","wikidata":"https://www.wikidata.org/wiki/Q671554","display_name":"Stroke (engine)","level":2,"score":0.6159346699714661},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5888593196868896},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5590636730194092},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.47492465376853943},{"id":"https://openalex.org/C27415008","wikidata":"https://www.wikidata.org/wiki/Q7256382","display_name":"Psychological intervention","level":2,"score":0.47379180788993835},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4396735727787018},{"id":"https://openalex.org/C11783203","wikidata":"https://www.wikidata.org/wiki/Q5478027","display_name":"Framingham Risk Score","level":3,"score":0.4183945059776306},{"id":"https://openalex.org/C1862650","wikidata":"https://www.wikidata.org/wiki/Q186005","display_name":"Physical therapy","level":1,"score":0.37658485770225525},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3115011155605316},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30214694142341614},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.1922900676727295},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.11475872993469238},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001281","descriptor_name":"Atrial Fibrillation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001281","descriptor_name":"Atrial Fibrillation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001281","descriptor_name":"Atrial Fibrillation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D002546","descriptor_name":"Ischemic Attack, Transient","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D002546","descriptor_name":"Ischemic Attack, Transient","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D002546","descriptor_name":"Ischemic Attack, Transient","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D002681","descriptor_name":"China","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D002681","descriptor_name":"China","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D002681","descriptor_name":"China","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012307","descriptor_name":"Risk Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012307","descriptor_name":"Risk Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012307","descriptor_name":"Risk Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D020521","descriptor_name":"Stroke","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D020521","descriptor_name":"Stroke","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D020521","descriptor_name":"Stroke","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/embc.2019.8857657","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc.2019.8857657","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:31946151","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31946151","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9300000071525574,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1566376227","https://openalex.org/W2060947741","https://openalex.org/W2096420978","https://openalex.org/W2125055259","https://openalex.org/W2140695747","https://openalex.org/W2148143831","https://openalex.org/W2149847034","https://openalex.org/W2291716819","https://openalex.org/W2498119267","https://openalex.org/W2539155824","https://openalex.org/W2772028634","https://openalex.org/W2796009016","https://openalex.org/W2797868822","https://openalex.org/W2806230779","https://openalex.org/W6633912811"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W3135126032","https://openalex.org/W1924178503","https://openalex.org/W4367335965","https://openalex.org/W4385574838","https://openalex.org/W4367335937","https://openalex.org/W4308573183"],"abstract_inverted_index":{"With":[0],"the":[1,25,32,125,146,150,159,167,203,211,232],"character":[2],"of":[3,27,29,74,83,96,127,132,140],"high":[4,6,9],"incidence,":[5],"prevalence":[7],"and":[8,19,37,45,80,143,162,165,174,189,197,205,222,235,239],"mortality,":[10],"stroke":[11,35,42,63,97,116,133,141,194],"has":[12],"brought":[13],"a":[14,94],"heavy":[15],"burden":[16],"to":[17,114],"families":[18],"society":[20],"in":[21,124,137,145,227],"China.":[22,59],"In":[23,60,153],"2009,":[24],"Ministry":[26],"Health":[28],"China":[30,33],"launched":[31],"national":[34,151],"screening":[36,233],"intervention":[38,240],"program,":[39,62],"which":[40],"screens":[41],"risk":[43,64,90,117,128,134,195],"factors":[44,65,91],"conducts":[46],"high-risk":[47],"population":[48],"interventions":[49,142],"for":[50,111,119,193],"people":[51,120],"aged":[52],"over":[53,58],"40":[54],"years":[55],"old":[56],"all":[57],"this":[61,112,154,228],"include":[66],"hypertension,":[67],"diabetes,":[68],"dyslipidemia,":[69],"atrial":[70],"fibrillation,":[71],"smoking,":[72],"lack":[73],"exercise,":[75],"apparently":[76],"overweight":[77],"or":[78,92,98],"obese":[79],"family":[81],"history":[82,95],"stroke.":[84],"People":[85],"with":[86,93,121],"more":[87],"than":[88],"two":[89],"transient":[99],"ischemic":[100],"attack":[101],"(TIA)":[102],"are":[103],"considered":[104],"as":[105],"high-risk.":[106],"However,":[107],"it":[108],"is":[109],"impossible":[110],"criterion":[113],"classify":[115],"level":[118],"\"unknown\"":[122],"values":[123],"fields":[126],"factors.":[129],"The":[130,207,224],"missing":[131],"levels":[135],"results":[136,148,208],"reduced":[138],"efficiency":[139,234],"inaccuracies":[144],"statistical":[147],"at":[149],"level.":[152],"paper,":[155],"firstly,":[156],"we":[157,178],"construct":[158],"training":[160,169],"set":[161,164,170],"test":[163],"process":[166],"imbalanced":[168],"based":[171,201,213],"on":[172,202,214],"oversampling":[173],"undersampling":[175],"method.":[176],"Then,":[177],"develop":[179],"logistic":[180],"regression":[181],"model,":[182,185],"decision":[183],"tree":[184],"neural":[186],"network":[187],"model":[188,192,212],"random":[190,215],"forest":[191,216],"classification,":[196],"evaluate":[198],"these":[199],"models":[200,225],"recall":[204,221],"precision.":[206,223],"show":[209],"that":[210],"achieves":[217],"best":[218],"performance":[219],"considering":[220],"constructed":[226],"paper":[229],"can":[230],"improve":[231],"avoid":[236],"unnecessary":[237],"rescreening":[238],"expenditures.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2019-10-18T00:00:00"}