{"id":"https://openalex.org/W3110815301","doi":"https://doi.org/10.1186/s12911-020-01151-9","title":"Treatment effect prediction with adversarial deep learning using electronic health records","display_name":"Treatment effect prediction with adversarial deep learning using electronic health records","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3110815301","doi":"https://doi.org/10.1186/s12911-020-01151-9","mag":"3110815301","pmid":"https://pubmed.ncbi.nlm.nih.gov/33317502"},"language":"en","primary_location":{"id":"doi:10.1186/s12911-020-01151-9","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12911-020-01151-9","pdf_url":"https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-020-01151-9","source":{"id":"https://openalex.org/S107516304","display_name":"BMC Medical Informatics and Decision Making","issn_l":"1472-6947","issn":["1472-6947"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Medical Informatics and Decision Making","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-020-01151-9","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051501904","display_name":"Jiebin Chu","orcid":"https://orcid.org/0000-0003-3651-2668"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiebin Chu","raw_affiliation_strings":["College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101881656","display_name":"Wei Dong","orcid":"https://orcid.org/0000-0003-4525-1105"},"institutions":[{"id":"https://openalex.org/I2802939634","display_name":"Chinese PLA General Hospital","ror":"https://ror.org/04gw3ra78","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2802939634"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Dong","raw_affiliation_strings":["Department of Cardiology, Chinese PLA General Hospital, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cardiology, Chinese PLA General Hospital, Beijing, China","institution_ids":["https://openalex.org/I2802939634"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100632265","display_name":"Jin-Liang Wang","orcid":"https://orcid.org/0000-0003-1574-1875"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinliang Wang","raw_affiliation_strings":["Cardiocloud Medical Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cardiocloud Medical Technology, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027802880","display_name":"Kunlun He","orcid":"https://orcid.org/0000-0002-3335-5700"},"institutions":[{"id":"https://openalex.org/I2802939634","display_name":"Chinese PLA General Hospital","ror":"https://ror.org/04gw3ra78","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2802939634"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kunlun He","raw_affiliation_strings":["Department of Cardiology, Chinese PLA General Hospital, Beijing, China. kunlunhe@plagh.org","Department of Cardiology, Chinese PLA General Hospital, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cardiology, Chinese PLA General Hospital, Beijing, China. kunlunhe@plagh.org","institution_ids":["https://openalex.org/I2802939634"]},{"raw_affiliation_string":"Department of Cardiology, Chinese PLA General Hospital, Beijing, China","institution_ids":["https://openalex.org/I2802939634"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056505424","display_name":"Zhengxing Huang","orcid":"https://orcid.org/0000-0002-2644-8642"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhengxing Huang","raw_affiliation_strings":["College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China. zhengxing.h@gmail.com","College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-2644-8642","affiliations":[{"raw_affiliation_string":"College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China. zhengxing.h@gmail.com","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5027802880","https://openalex.org/A5056505424"],"corresponding_institution_ids":["https://openalex.org/I2802939634","https://openalex.org/I76130692"],"apc_list":{"value":1570,"currency":"GBP","value_usd":1925},"apc_paid":{"value":1570,"currency":"GBP","value_usd":1925},"fwci":1.7606,"has_fulltext":true,"cited_by_count":26,"citation_normalized_percentile":{"value":0.88407883,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"20","issue":"S4","first_page":"139","last_page":"139"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998000264167786,"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":0.9998000264167786,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9434999823570251,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7113555073738098},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.674912691116333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.639560878276825},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6297906041145325},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6043936610221863},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5851370096206665},{"id":"https://openalex.org/keywords/health-informatics","display_name":"Health informatics","score":0.5470408797264099},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4589120149612427},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4506046772003174},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4331444203853607},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.42693841457366943},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2988337576389313},{"id":"https://openalex.org/keywords/public-health","display_name":"Public health","score":0.22780689597129822}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7113555073738098},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.674912691116333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.639560878276825},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6297906041145325},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6043936610221863},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5851370096206665},{"id":"https://openalex.org/C145642194","wikidata":"https://www.wikidata.org/wiki/Q870895","display_name":"Health informatics","level":3,"score":0.5470408797264099},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4589120149612427},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4506046772003174},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4331444203853607},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.42693841457366943},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2988337576389313},{"id":"https://openalex.org/C138816342","wikidata":"https://www.wikidata.org/wiki/Q189603","display_name":"Public health","level":2,"score":0.22780689597129822},{"id":"https://openalex.org/C159110408","wikidata":"https://www.wikidata.org/wiki/Q121176","display_name":"Nursing","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006333","descriptor_name":"Heart Failure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006333","descriptor_name":"Heart Failure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006333","descriptor_name":"Heart Failure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006333","descriptor_name":"Heart Failure","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"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":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016015","descriptor_name":"Logistic Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016015","descriptor_name":"Logistic Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016015","descriptor_name":"Logistic Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016015","descriptor_name":"Logistic Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D054058","descriptor_name":"Acute Coronary Syndrome","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D054058","descriptor_name":"Acute Coronary Syndrome","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D054058","descriptor_name":"Acute Coronary Syndrome","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D054058","descriptor_name":"Acute Coronary Syndrome","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":4,"locations":[{"id":"doi:10.1186/s12911-020-01151-9","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12911-020-01151-9","pdf_url":"https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-020-01151-9","source":{"id":"https://openalex.org/S107516304","display_name":"BMC Medical Informatics and Decision Making","issn_l":"1472-6947","issn":["1472-6947"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Medical Informatics and Decision Making","raw_type":"journal-article"},{"id":"pmid:33317502","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33317502","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":"BMC medical informatics and decision making","raw_type":null},{"id":"pmh:oai:doaj.org/article:9528e374b5264807bc9036418ed56203","is_oa":true,"landing_page_url":"https://doaj.org/article/9528e374b5264807bc9036418ed56203","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Medical Informatics and Decision Making, Vol 20, Iss S4, Pp 1-14 (2020)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:7735418","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7735418","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Med Inform Decis Mak","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1186/s12911-020-01151-9","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12911-020-01151-9","pdf_url":"https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-020-01151-9","source":{"id":"https://openalex.org/S107516304","display_name":"BMC Medical Informatics and Decision Making","issn_l":"1472-6947","issn":["1472-6947"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Medical Informatics and Decision Making","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7200000286102295}],"awards":[{"id":"https://openalex.org/G5732429111","display_name":"\u57fa\u4e8e\u7535\u5b50\u75c5\u5386\u6570\u636e\u6316\u6398\u7684\u6025\u6027\u51a0\u8109\u7efc\u5408\u5f81\u98ce\u9669\u8bc4\u4f30\u53ca\u5e72\u9884\u6548\u679c\u5206\u6790\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61672450","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7469537926","display_name":null,"funder_award_id":"2018YFC2001204","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3110815301.pdf","grobid_xml":"https://content.openalex.org/works/W3110815301.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W174941419","https://openalex.org/W635046673","https://openalex.org/W1580457945","https://openalex.org/W1594294799","https://openalex.org/W1835900096","https://openalex.org/W1981276685","https://openalex.org/W2023155012","https://openalex.org/W2024828837","https://openalex.org/W2034690624","https://openalex.org/W2043464907","https://openalex.org/W2053622038","https://openalex.org/W2064903582","https://openalex.org/W2069255298","https://openalex.org/W2071738728","https://openalex.org/W2073742300","https://openalex.org/W2084917404","https://openalex.org/W2088198811","https://openalex.org/W2117677020","https://openalex.org/W2130566074","https://openalex.org/W2141115552","https://openalex.org/W2145094598","https://openalex.org/W2148092884","https://openalex.org/W2150291618","https://openalex.org/W2161330753","https://openalex.org/W2166303835","https://openalex.org/W2188353343","https://openalex.org/W2208550830","https://openalex.org/W2293628088","https://openalex.org/W2305754340","https://openalex.org/W2319983832","https://openalex.org/W2389937032","https://openalex.org/W2515189678","https://openalex.org/W2531479711","https://openalex.org/W2548641835","https://openalex.org/W2568394902","https://openalex.org/W2608226954","https://openalex.org/W2620393362","https://openalex.org/W2716974933","https://openalex.org/W2739340771","https://openalex.org/W2744140371","https://openalex.org/W2785777814","https://openalex.org/W2788624168","https://openalex.org/W2805089815","https://openalex.org/W2883148267","https://openalex.org/W2892019986","https://openalex.org/W2901121110","https://openalex.org/W2901653602","https://openalex.org/W2951004968","https://openalex.org/W2964200294"],"related_works":["https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4389832810","https://openalex.org/W4220682630","https://openalex.org/W3181622257","https://openalex.org/W3163146846"],"abstract_inverted_index":{"BACKGROUND:":[0],"Treatment":[1],"effect":[2,86,195,219],"prediction":[3,87,196,220],"(TEP)":[4],"plays":[5],"an":[6,82],"important":[7],"role":[8],"in":[9,59,206,263],"disease":[10],"management":[11],"by":[12,124,136,282],"ensuring":[13],"that":[14,252],"the":[15,37,56,65,91,100,118,126,130,152,155,175,190,209,212,241,265,279,302,328,340],"expected":[16],"clinical":[17,32,53,171],"outcomes":[18],"are":[19],"obtained":[20],"after":[21],"performing":[22],"specialized":[23],"and":[24,80,102,109,133,202,227,308,314],"sophisticated":[25],"treatments":[26,110,135,309],"on":[27,151,168,184,330],"patients":[28],"given":[29],"their":[30,321],"personalized":[31],"status.":[33],"In":[34,182,269],"recent":[35],"years,":[36],"wide":[38],"adoption":[39],"of":[40,67,72,105,117,138,154,178,208,287,317,335,342],"electronic":[41],"health":[42],"records":[43],"(EHRs)":[44],"has":[45],"provided":[46],"a":[47,69,139,145,179,274,284],"comprehensive":[48],"data":[49,75,289],"source":[50],"for":[51,98,160,235],"intelligent":[52],"applications":[54],"including":[55],"TEP":[57,266,280],"investigated":[58],"this":[60,270],"study.":[61],"METHOD:":[62],"We":[63],"examined":[64],"problem":[66,281],"using":[68],"large":[70,285],"volume":[71,286],"heterogeneous":[73],"EHR":[74,112,322],"to":[76,89,260,277,310,347],"predict":[77],"treatment":[78,85,194,218],"effects":[79],"developed":[81],"adversarial":[83,141,192,293],"deep":[84,193,217],"model":[88,94,165,255,276,298,329],"address":[90,278],"problem.":[92,267],"Our":[93],"employed":[95],"two":[96,169,336],"auto-encoders":[97],"learning":[99,142,294],"representative":[101],"discriminative":[103,115,315],"features":[104,120],"both":[106],"patient":[107,131,306,318],"characteristics":[108,132],"from":[111,174,290,320],"data.":[113,323],"The":[114,163,248,332],"power":[116],"learned":[119],"was":[121,149,166],"further":[122,300],"enhanced":[123],"decoding":[125],"correlational":[127,303],"information":[128,304],"between":[129,305],"subsequent":[134],"means":[137],"generated":[140],"strategy.":[143],"Thereafter,":[144],"logistic":[146,223],"regression":[147,224],"layer":[148,159],"appended":[150],"top":[153],"resulting":[156],"feature":[157],"representation":[158,316,325],"TEP.":[161,331],"RESULT:":[162],"proposed":[164,191,242,254,297,344],"evaluated":[167],"real":[170],"datasets":[172],"collected":[173],"cardiology":[176],"department":[177],"Chinese":[180],"hospital.":[181],"particular,":[183],"acute":[185],"coronary":[186],"syndrome":[187],"(ACS)":[188],"dataset,":[189],"(ADTEP)":[197],"(0.662)":[198],"exhibited":[199],"1.4,":[200],"2.2,":[201],"6.3%":[203],"performance":[204,258],"gains":[205],"terms":[207],"area":[210],"under":[211],"ROC":[213],"curve":[214],"(AUC)":[215],"over":[216],"(DTEP)":[221],"(0.653),":[222],"(LR)":[225],"(0.648),":[226],"support":[228],"vector":[229],"machine":[230],"(SVM)":[231],"(0.621),":[232],"respectively.":[233],"As":[234],"heart":[236],"failure":[237],"(HF)":[238],"case":[239,337],"study,":[240],"ADTEP":[243],"also":[244],"outperformed":[245],"all":[246],"benchmarks.":[247],"experimental":[249,333],"results":[250,334],"demonstrated":[251],"our":[253,296,343],"achieved":[256],"competitive":[257],"compared":[259,346],"state-of-the-art":[261,348],"models":[262],"tackling":[264],"CONCLUSION:":[268],"work,":[271],"we":[272],"propose":[273],"novel":[275],"utilizing":[283],"observational":[288],"EHR.":[291],"With":[292],"strategy,":[295],"can":[299],"explore":[301],"statuses":[307],"extract":[311],"more":[312],"robust":[313],"samples":[319],"Such":[324],"finally":[326],"benefits":[327],"studies":[338],"demonstrate":[339],"superiority":[341],"method":[345],"methods.":[349]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-19T15:47:20.252518","created_date":"2020-12-21T00:00:00"}
