{"id":"https://openalex.org/W2787487383","doi":"https://doi.org/10.1145/3219819.3219909","title":"Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models","display_name":"Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models","publication_year":2018,"publication_date":"2018-07-19","ids":{"openalex":"https://openalex.org/W2787487383","doi":"https://doi.org/10.1145/3219819.3219909","mag":"2787487383"},"language":"en","primary_location":{"id":"doi:10.1145/3219819.3219909","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219819.3219909","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3219909","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3219909","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047159613","display_name":"Mengying Sun","orcid":"https://orcid.org/0000-0002-8947-0345"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mengying Sun","raw_affiliation_strings":["Michigan State University, East Lansing, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101428692","display_name":"Fengyi Tang","orcid":"https://orcid.org/0000-0002-7431-979X"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fengyi Tang","raw_affiliation_strings":["Michigan State University, East Lansing, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030837133","display_name":"Jinfeng Yi","orcid":"https://orcid.org/0000-0003-2149-0670"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinfeng Yi","raw_affiliation_strings":["JD AI Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD AI Research, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455768","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0001-9459-9461"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Weill Cornell Medical School, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weill Cornell Medical School, New York, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047215778","display_name":"Jiayu Zhou","orcid":"https://orcid.org/0000-0003-4336-6777"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiayu Zhou","raw_affiliation_strings":["Michigan State University, East Lansing, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.4053,"has_fulltext":true,"cited_by_count":57,"citation_normalized_percentile":{"value":0.96477322,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"793","last_page":"801"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9991999864578247,"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":0.9991999864578247,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.972100019454956,"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/T12296","display_name":"Autopsy Techniques and Outcomes","score":0.960099995136261,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/computer-science","display_name":"Computer science","score":0.7742600440979004},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7703929543495178},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7382376790046692},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6614627838134766},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6111308932304382},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6104332804679871},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5864050984382629},{"id":"https://openalex.org/keywords/medical-record","display_name":"Medical record","score":0.5685886740684509},{"id":"https://openalex.org/keywords/health-informatics","display_name":"Health informatics","score":0.46818697452545166},{"id":"https://openalex.org/keywords/informatics","display_name":"Informatics","score":0.4478326439857483},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.41692298650741577},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3491813540458679},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34630337357521057},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.19954398274421692},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.16485992074012756},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10334247350692749}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7742600440979004},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7703929543495178},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7382376790046692},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6614627838134766},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6111308932304382},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6104332804679871},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5864050984382629},{"id":"https://openalex.org/C195910791","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Medical record","level":2,"score":0.5685886740684509},{"id":"https://openalex.org/C145642194","wikidata":"https://www.wikidata.org/wiki/Q870895","display_name":"Health informatics","level":3,"score":0.46818697452545166},{"id":"https://openalex.org/C191630685","wikidata":"https://www.wikidata.org/wiki/Q4027615","display_name":"Informatics","level":2,"score":0.4478326439857483},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.41692298650741577},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3491813540458679},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34630337357521057},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.19954398274421692},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.16485992074012756},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10334247350692749},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","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/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3219819.3219909","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219819.3219909","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3219909","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3219819.3219909","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219819.3219909","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3219909","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1067560820","display_name":null,"funder_award_id":"IIS-1750326","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1814114463","display_name":null,"funder_award_id":"N00014-14-1-0631","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G1941980000","display_name":null,"funder_award_id":"IIS-1650723","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2070593852","display_name":"EAGER: Patient Similarity Learning with Massive Clinical Data and Its Applications in Cohort Identification","funder_award_id":"1650723","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3232552192","display_name":"III: Small: Collaborative Research: Structured Methods for Multi-Task Learning","funder_award_id":"1615597","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3916121238","display_name":"CAREER: Interpretable Deep Modeling of Discrete Time Event Sequences","funder_award_id":"1750326","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4504108201","display_name":null,"funder_award_id":"N00014-17-1","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G5053037589","display_name":null,"funder_award_id":"IIS-1565596, IIS-1615597, IIS-1749940, IIS-1650723, IIS-1716432, IIS-1750326","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5395595088","display_name":null,"funder_award_id":"N00014-17-1-2265","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G5794704315","display_name":null,"funder_award_id":"IIS-1716432","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6677749066","display_name":null,"funder_award_id":"IIS-1749940","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6832078411","display_name":"III: Small: Collaborative Research: Comprehensive Heterogeneous Response Regression from Complex Data","funder_award_id":"1716432","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7617836800","display_name":null,"funder_award_id":"IIS-1615597","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7743751914","display_name":null,"funder_award_id":"IIS-1565596","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8541415355","display_name":"CRII: III: Integrating Domain Knowledge via Interactive Multi-Task Learning","funder_award_id":"1565596","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8858239272","display_name":"CAREER: Harness the Big Data via Large-Scale Lifelong Learning","funder_award_id":"1749940","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2787487383.pdf","grobid_xml":"https://content.openalex.org/works/W2787487383.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1932198206","https://openalex.org/W1945616565","https://openalex.org/W1998249118","https://openalex.org/W2042954874","https://openalex.org/W2064675550","https://openalex.org/W2099163653","https://openalex.org/W2100556411","https://openalex.org/W2103106495","https://openalex.org/W2157331557","https://openalex.org/W2179402106","https://openalex.org/W2180612164","https://openalex.org/W2243397390","https://openalex.org/W2255847468","https://openalex.org/W2284851926","https://openalex.org/W2396881363","https://openalex.org/W2404901863","https://openalex.org/W2481271618","https://openalex.org/W2528572867","https://openalex.org/W2570685808","https://openalex.org/W2618098489","https://openalex.org/W2621053657","https://openalex.org/W2625625371","https://openalex.org/W2742491462","https://openalex.org/W2744095836","https://openalex.org/W2746600820","https://openalex.org/W2766108848","https://openalex.org/W2779221219","https://openalex.org/W2784452215","https://openalex.org/W2950035161","https://openalex.org/W2963098487","https://openalex.org/W2963612069","https://openalex.org/W2963834268","https://openalex.org/W2963857521","https://openalex.org/W2985962305","https://openalex.org/W3101973032","https://openalex.org/W3106412272","https://openalex.org/W4297775706"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"The":[0,166],"surging":[1],"availability":[2],"of":[3,37,79,88,109,158,205],"electronic":[4],"medical":[5,14,80,100,156],"records":[6,157],"(EHR)":[7],"leads":[8],"to":[9,63,72,154,174,178],"increased":[10],"research":[11],"interests":[12],"in":[13],"predictive":[15,93],"modeling.":[16],"Recently":[17],"many":[18],"deep":[19,43,57,92,110],"learning":[20],"based":[21],"predicted":[22],"models":[23,44],"are":[24,45,70],"also":[25],"developed":[26],"for":[27],"EHR":[28,146],"data":[29],"and":[30,132,148,160,164,201],"demonstrated":[31],"impressive":[32],"performance.":[33],"However,":[34],"a":[35,55,89,98,116,137,196],"series":[36],"recent":[38],"studies":[39,194],"showed":[40],"that":[41,135,181],"these":[42],"not":[46,104,187],"safe:":[47],"they":[48],"suffer":[49],"from":[50],"certain":[51],"vulnerabilities.":[52],"In":[53,76,125],"short,":[54],"well-trained":[56],"network":[58],"can":[59,170,182],"be":[60],"extremely":[61],"sensitive":[62],"inputs":[64,69],"with":[65,145],"negligible":[66],"changes.":[67],"These":[68],"referred":[71],"as":[73],"adversarial":[74],"examples.":[75],"the":[77,86,107,123,142,179,203,206],"context":[78],"informatics,":[81],"such":[82],"attacks":[83],"could":[84],"alter":[85],"result":[87],"high":[90],"performance":[91],"model":[94,144],"by":[95],"slightly":[96],"perturbing":[97],"patient's":[99],"records.":[101],"Such":[102],"instability":[103],"only":[105],"reflects":[106],"weakness":[108],"architectures,":[111],"more":[112],"importantly,":[113],"it":[114],"offers":[115],"guide":[117],"on":[118,122,195],"detecting":[119],"susceptible":[120,162],"parts":[121],"inputs.":[124],"this":[126,151],"paper,":[127],"we":[128,149],"propose":[129],"an":[130],"efficient":[131,167],"effective":[133],"framework":[134],"learns":[136],"time-preferential":[138],"minimum":[139],"attack":[140,152],"targeting":[141],"LSTM":[143],"inputs,":[147],"leverage":[150],"strategy":[153],"screen":[155],"patients":[159],"identify":[161],"events":[163],"measurements.":[165],"screening":[168],"procedure":[169],"assist":[171],"decision":[172],"makers":[173],"pay":[175],"extra":[176],"attentions":[177],"locations":[180],"cause":[183],"severe":[184],"consequence":[185],"if":[186],"measured":[188],"correctly.":[189],"We":[190],"conduct":[191],"extensive":[192],"empirical":[193],"real-world":[197],"urgent":[198],"care":[199],"cohort":[200],"demonstrate":[202],"effectiveness":[204],"proposed":[207],"screening.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
