{"id":"https://openalex.org/W1998249118","doi":"https://doi.org/10.1145/2623330.2623711","title":"From micro to macro","display_name":"From micro to macro","publication_year":2014,"publication_date":"2014-08-22","ids":{"openalex":"https://openalex.org/W1998249118","doi":"https://doi.org/10.1145/2623330.2623711","mag":"1998249118"},"language":"en","primary_location":{"id":"doi:10.1145/2623330.2623711","doi":"https://doi.org/10.1145/2623330.2623711","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2623330.2623711","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"volume":null,"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/A5047215778","display_name":"Jiayu Zhou","orcid":"https://orcid.org/0000-0003-4336-6777","observed_orcids":["https://orcid.org/0000-0003-4336-6777"]},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiayu Zhou","raw_affiliation_strings":["Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455871","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0002-7594-663X","observed_orcids":["https://orcid.org/0000-0002-7594-663X"]},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#","institution_ids":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103540584","display_name":"Jianying Hu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianying Hu","raw_affiliation_strings":["IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#","institution_ids":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010419481","display_name":"Jieping Ye","orcid":"https://orcid.org/0000-0001-8662-5818","observed_orcids":["https://orcid.org/0000-0001-8662-5818"]},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jieping Ye","raw_affiliation_strings":["Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.3793,"has_fulltext":false,"cited_by_count":114,"citation_normalized_percentile":{"value":0.99176142,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"135","last_page":"144"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9976000189781189,"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":0.9976000189781189,"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":0.0010000000474974513,"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/T12246","display_name":"Chronic Disease Management Strategies","score":0.00019999999494757503,"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/patient-phenotyping","display_name":"patient phenotyping","score":0.8169999718666077},{"id":"https://openalex.org/keywords/electronic-health-records","display_name":"electronic health records","score":0.9879999756813049},{"id":"https://openalex.org/keywords/sparse-longitudinal-data","display_name":"sparse longitudinal data","score":0.7429999709129333},{"id":"https://openalex.org/keywords/latent-structure","display_name":"latent structure","score":0.39800000190734863},{"id":"https://openalex.org/keywords/personalized-medicine","display_name":"personalized medicine","score":0.6759999990463257},{"id":"https://openalex.org/keywords/pacifiers","display_name":"pacifiers","score":0.4970000088214874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6486493349075317},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5235254764556885},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.480106383562088},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4784640967845917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4225865602493286},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3925371766090393},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3906642496585846},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.17570555210113525},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11277592182159424},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2623330.2623711","doi":"https://doi.org/10.1145/2623330.2623711","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2623330.2623711","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9074000120162964,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"sustainable_development_goals_aurora":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G4278224465","display_name":"Computational Methods for Expression Image Analysis","funder_award_id":"R01LM010730","funder_id":"https://openalex.org/F4320337372","funder_display_name":"U.S. National Library of Medicine"},{"id":"https://openalex.org/G5497785876","display_name":null,"funder_award_id":"IIS-0953662","funder_id":"https://openalex.org/F4320337389","funder_display_name":"Division of Information and Intelligent Systems"},{"id":"https://openalex.org/G6818664488","display_name":null,"funder_award_id":"MCB-1026710","funder_id":"https://openalex.org/F4320337397","funder_display_name":"Division of Molecular and Cellular Biosciences"},{"id":"https://openalex.org/G968814932","display_name":null,"funder_award_id":"CCF-1025177","funder_id":"https://openalex.org/F4320337387","funder_display_name":"Division of Computing and Communication Foundations"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337372","display_name":"U.S. National Library of Medicine","ror":"https://ror.org/0060t0j89"},{"id":"https://openalex.org/F4320337387","display_name":"Division of Computing and Communication Foundations","ror":"https://ror.org/01mng8331"},{"id":"https://openalex.org/F4320337389","display_name":"Division of Information and Intelligent Systems","ror":"https://ror.org/053a2cp42"},{"id":"https://openalex.org/F4320337397","display_name":"Division of Molecular and Cellular Biosciences","ror":"https://ror.org/002jdaq33"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W3219084","https://openalex.org/W263545290","https://openalex.org/W1500188831","https://openalex.org/W1573301526","https://openalex.org/W1582261060","https://openalex.org/W1965701254","https://openalex.org/W1973825638","https://openalex.org/W2012451988","https://openalex.org/W2013009184","https://openalex.org/W2014898084","https://openalex.org/W2031250362","https://openalex.org/W2031967811","https://openalex.org/W2032612424","https://openalex.org/W2042775700","https://openalex.org/W2045879331","https://openalex.org/W2055491774","https://openalex.org/W2060204507","https://openalex.org/W2068548513","https://openalex.org/W2080320419","https://openalex.org/W2082269898","https://openalex.org/W2095526444","https://openalex.org/W2103106495","https://openalex.org/W2103972604","https://openalex.org/W2108016040","https://openalex.org/W2111700774","https://openalex.org/W2118099552","https://openalex.org/W2118718620","https://openalex.org/W2120166750","https://openalex.org/W2123503703","https://openalex.org/W2126607811","https://openalex.org/W2137245235","https://openalex.org/W2146130798","https://openalex.org/W2170865247","https://openalex.org/W2174160981","https://openalex.org/W2401764675","https://openalex.org/W2611328865","https://openalex.org/W2951095867","https://openalex.org/W2963636509","https://openalex.org/W3003365835","https://openalex.org/W7070499600","https://openalex.org/W7096602934"],"related_works":["https://openalex.org/W2556399337","https://openalex.org/W2991319174","https://openalex.org/W2998463436","https://openalex.org/W2890788091","https://openalex.org/W2135496322","https://openalex.org/W3196995059","https://openalex.org/W7097379378","https://openalex.org/W2754794949","https://openalex.org/W7027853733","https://openalex.org/W2606287704"],"abstract_inverted_index":{"Inferring":[0],"phenotypic":[1],"patterns":[2],"from":[3,241,248,266],"population-scale":[4],"clinical":[5],"data":[6,22,65,80],"is":[7,31,117,146,162,185],"a":[8,64,85,89,93,120,173],"core":[9],"computational":[10],"task":[11],"in":[12,127,226],"the":[13,38,77,104,123,153,169,203,223,234],"development":[14],"of":[15,21,29,81,107,119,122,176,187,205,208],"personalized":[16],"medicine.":[17],"One":[18,145],"important":[19],"source":[20],"on":[23,196,245,252,255],"which":[24,46,151,167],"to":[25,55,141,243,250],"conduct":[26],"this":[27,60],"type":[28],"research":[30],"patient":[32,39,57,83,100,129,170],"Electronic":[33],"Medical":[34],"Records":[35],"(EMR).":[36],"However,":[37],"EMRs":[40],"are":[41,155],"typically":[42],"sparse":[43,86],"and":[44,92,96,213,247],"noisy,":[45],"creates":[47],"significant":[48],"challenges":[49],"if":[50],"we":[51,62,75,111,193],"use":[52],"them":[53],"directly":[54],"represent":[56],"phenotypes.":[58,177],"In":[59],"paper,":[61],"propose":[63,138],"driven":[66],"phenotyping":[67],"framework":[68],"called":[69],"Pacifier":[70,195],"(PAtient":[71],"reCord":[72],"densIFIER),":[73],"where":[74],"interpret":[76],"longitudinal":[78],"EMR":[79,200],"each":[82,114],"as":[84],"matrix":[87],"with":[88],"feature":[90],"dimension":[91],"time":[94],"dimension,":[95],"derive":[97],"more":[98],"robust":[99],"phenotypes":[101,154,264],"by":[102,233],"exploring":[103],"latent":[105],"structure":[106],"those":[108],"matrices.":[109],"Specifically,":[110],"assume":[112],"that":[113,184,222],"derived":[115,265],"phenotype":[116],"composed":[118],"subset":[121],"medical":[124],"features":[125],"contained":[126],"original":[128],"EMR,":[130],"whose":[131],"value":[132],"evolves":[133],"smoothly":[134],"over":[135],"time.":[136],"We":[137,178,259],"two":[139,197],"formulations":[140],"achieve":[142],"such":[143],"goal.":[144],"Individual":[147],"Basis":[148,164],"Approach":[149,165],"(IBA),":[150],"assumes":[152,168],"different":[156],"for":[157,202],"every":[158],"patient.":[159],"The":[160],"other":[161],"Shared":[163],"(SBA),":[166],"population":[171],"shares":[172],"common":[174],"set":[175],"develop":[179],"an":[180],"efficient":[181],"optimization":[182],"algorithm":[183],"capable":[186],"resolving":[188],"both":[189,227],"problems":[190],"efficiently.":[191],"Finally":[192],"validate":[194],"real":[198],"world":[199],"cohorts":[201],"tasks":[204,228],"early":[206],"prediction":[207],"Congestive":[209],"Heart":[210],"Failure":[211],"(CHF)":[212],"End":[214],"Stage":[215],"Renal":[216],"Disease":[217],"(ESRD).":[218],"Our":[219],"results":[220],"show":[221],"predictive":[224],"performance":[225],"can":[229],"be":[230],"improved":[231,240],"significantly":[232],"proposed":[235],"algorithms":[236],"(average":[237],"AUC":[238],"score":[239],"0.689":[242],"0.816":[244],"CHF,":[246],"0.756":[249],"0.838":[251],"ESRD":[253],"respectively,":[254],"diagnosis":[256],"group":[257],"granularity).":[258],"also":[260],"illustrate":[261],"some":[262],"interesting":[263],"our":[267],"data.":[268]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":22},{"year":2016,"cited_by_count":19},{"year":2015,"cited_by_count":15},{"year":2014,"cited_by_count":2}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2016-06-24T00:00:00"}
