{"id":"https://openalex.org/W2951969182","doi":"https://doi.org/10.1145/3292500.3330718","title":"A Robust Framework for Accelerated Outcome-driven Risk Factor Identification from EHR","display_name":"A Robust Framework for Accelerated Outcome-driven Risk Factor Identification from EHR","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2951969182","doi":"https://doi.org/10.1145/3292500.3330718","mag":"2951969182"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330718","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"article","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/A5103154375","display_name":"Prithwish Chakraborty","orcid":"https://orcid.org/0000-0003-1407-7677"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Prithwish Chakraborty","raw_affiliation_strings":["IBM, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM, Yorktown Heights, NY, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025508406","display_name":"Faisal Farooq","orcid":"https://orcid.org/0000-0002-3551-7371"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Faisal Farooq","raw_affiliation_strings":["IBM, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM, Yorktown Heights, NY, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103154375"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9801,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.81956927,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1800","last_page":"1808"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9987999796867371,"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":0.9987999796867371,"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":0.9894000291824341,"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/identification","display_name":"Identification (biology)","score":0.6651852130889893},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.5418797731399536},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5313936471939087},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09298253059387207}],"concepts":[{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6651852130889893},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.5418797731399536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5313936471939087},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09298253059387207},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330718","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1257569579","https://openalex.org/W1786407144","https://openalex.org/W2004910511","https://openalex.org/W2036498800","https://openalex.org/W2080041330","https://openalex.org/W2092496994","https://openalex.org/W2134452881","https://openalex.org/W2345192301","https://openalex.org/W2810447904","https://openalex.org/W2998216295","https://openalex.org/W4249033934"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2978999882","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2153369162","https://openalex.org/W2119369480","https://openalex.org/W2382290278","https://openalex.org/W3141031773"],"abstract_inverted_index":{"Electronic":[0],"Health":[1],"Records":[2],"(EHR)":[3],"containing":[4],"longitudinal":[5],"information":[6],"about":[7],"millions":[8],"of":[9,33,74,80,117,138,187,268,282,291,324],"patient":[10,125],"lives":[11,126],"are":[12,261],"increasingly":[13],"being":[14,279],"utilized":[15],"by":[16,52,59,69,211],"organizations":[17],"across":[18],"the":[19,136,144,147,213,227,257,272,277,296],"healthcare":[20],"spectrum.":[21],"Studies":[22],"on":[23,108,220],"EHR":[24,112,120,173],"data":[25,49,133],"have":[26],"enabled":[27],"real":[28,222],"world":[29],"applications":[30],"like":[31],"understanding":[32],"disease":[34],"progression,":[35],"outcomes":[36],"analysis,":[37,75],"and":[38,62,78,129,159,177,243,308],"comparative":[39],"effectiveness":[40],"research.":[41],"However,":[42],"often":[43,63],"every":[44],"study":[45,58],"is":[46,50,67,143,238,264],"independently":[47],"commissioned,":[48],"gathered":[51],"surveys":[53],"or":[54],"specifically":[55],"purchased":[56],"per":[57],"a":[60,97,109,118,188,197,288],"long":[61],"painful":[64],"process.":[65],"This":[66,82],"followed":[68],"an":[70,163],"arduous":[71],"repetitive":[72],"cycle":[73],"model":[76],"building,":[77],"generation":[79],"insights.":[81],"process":[83],"can":[84,305],"take":[85],"anywhere":[86],"between":[87],"1":[88],"-":[89],"3":[90],"years.":[91],"In":[92],"this":[93,141,154],"paper,":[94],"we":[95],"present":[96],"robust":[98],"end-to-end":[99,164],"machine":[100],"learning":[101],"based":[102],"SaaS":[103],"system":[104,270,278,304],"to":[105,149,171,206,246,248,295,315],"perform":[106],"analysis":[107,174,307],"very":[110],"large":[111],"dataset.":[113],"The":[114,236],"framework":[115,142,166,228,237],"consists":[116,186],"proprietary":[119],"datamart":[121],"spanning":[122],"~55":[123],"million":[124],"in":[127,146,216,271,311],"USA":[128],"over":[130,241,252],"~20":[131],"billion":[132],"points.":[134],"To":[135],"best":[137],"our":[139,269,303],"knowledge,":[140],"largest":[145],"industry":[148,297],"analyze":[150],"medical":[151],"records":[152],"at":[153,175],"scale,":[155],"with":[156,167,196,201],"such":[157,302],"efficacy":[158],"ease.":[160],"We":[161],"developed":[162],"ML":[165,258],"carefully":[168],"chosen":[169],"components":[170],"support":[172],"scale":[176],"suitable":[178],"for":[179,266],"further":[180],"downstream":[181],"clinical":[182,198],"analysis.":[183],"Specifically,":[184],"it":[185],"ridge":[189],"regularized":[190],"Survival":[191],"Support":[192],"Vector":[193],"Machine":[194],"(SSVM)":[195],"kernel,":[199],"coupled":[200],"Chi-square":[202],"distance-based":[203],"feature":[204],"selection,":[205],"uncover":[207],"relevant":[208,230],"risk":[209],"factors":[210,231],"exploiting":[212],"weak":[214],"correlations":[215],"EHR.":[217],"Our":[218],"results":[219],"multiple":[221],"use":[223],"cases":[224],"indicate":[225],"that":[226],"identifies":[229],"effectively":[232],"without":[233],"expert":[234,254],"supervision.":[235],"stable,":[239],"generalizable":[240],"outcomes,":[242],"also":[244],"found":[245],"contribute":[247],"better":[249,312],"out-of-bound":[250],"prediction":[251],"known":[253],"features.":[255],"Importantly,":[256],"methodologies":[259],"used":[260],"interpretable":[262],"which":[263],"critical":[265],"acceptance":[267],"targeted":[273],"user":[274],"base.":[275],"With":[276],"operational,":[280],"all":[281],"these":[283],"studies":[284],"were":[285],"completed":[286],"within":[287],"time":[289],"frame":[290],"3-4":[292],"weeks":[293],"compared":[294],"standard":[298],"12-36":[299],"months.":[300],"As":[301],"accelerate":[306],"discovery,":[309],"result":[310],"ROI":[313],"due":[314],"reduced":[316],"investments":[317],"as":[318,320],"well":[319],"quicker":[321],"turn":[322],"around":[323],"studies.":[325]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}