{"id":"https://openalex.org/W2967986317","doi":"https://doi.org/10.3389/fdata.2019.00030","title":"Significant EHR Feature-Driven T2D Inference: Predictive Machine Learning and Networks","display_name":"Significant EHR Feature-Driven T2D Inference: Predictive Machine Learning and Networks","publication_year":2019,"publication_date":"2019-09-27","ids":{"openalex":"https://openalex.org/W2967986317","doi":"https://doi.org/10.3389/fdata.2019.00030","mag":"2967986317","pmid":"https://pubmed.ncbi.nlm.nih.gov/33693353"},"language":"en","primary_location":{"id":"doi:10.3389/fdata.2019.00030","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2019.00030","pdf_url":"https://www.frontiersin.org/articles/10.3389/fdata.2019.00030/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/articles/10.3389/fdata.2019.00030/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037753192","display_name":"Nicolo' Preo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicolo' Preo","raw_affiliation_strings":["Bip xScience, Milan, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bip xScience, Milan, Italy","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103256050","display_name":"Enrico Capobianco","orcid":"https://orcid.org/0000-0002-3532-1192"},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Enrico Capobianco","raw_affiliation_strings":["Center for Computational Science, University of Miami, Miami, FL, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Computational Science, University of Miami, Miami, FL, United States","institution_ids":["https://openalex.org/I145608581"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103256050"],"corresponding_institution_ids":["https://openalex.org/I145608581"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":0.5806,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75836591,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"2","issue":null,"first_page":"30","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9955999851226807,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.982200026512146,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7739574909210205},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6569817662239075},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6484054327011108},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5953705310821533},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5659018754959106},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5444194078445435},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.45594772696495056},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4124409556388855},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36992037296295166},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12080791592597961}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7739574909210205},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6569817662239075},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6484054327011108},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5953705310821533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5659018754959106},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5444194078445435},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.45594772696495056},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4124409556388855},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36992037296295166},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12080791592597961},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3389/fdata.2019.00030","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2019.00030","pdf_url":"https://www.frontiersin.org/articles/10.3389/fdata.2019.00030/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},{"id":"pmid:33693353","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33693353","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":"Frontiers in big data","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:7931876","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7931876","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":"Front Big Data","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3389/fdata.2019.00030","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2019.00030","pdf_url":"https://www.frontiersin.org/articles/10.3389/fdata.2019.00030/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2967986317.pdf","grobid_xml":"https://content.openalex.org/works/W2967986317.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1816688868","https://openalex.org/W1839682376","https://openalex.org/W2060758175","https://openalex.org/W2075894130","https://openalex.org/W2099111195","https://openalex.org/W2108982826","https://openalex.org/W2113242816","https://openalex.org/W2131668595","https://openalex.org/W2135046866","https://openalex.org/W2149199519","https://openalex.org/W2162980141","https://openalex.org/W2239135493","https://openalex.org/W2398417419","https://openalex.org/W2515789121","https://openalex.org/W2738759181","https://openalex.org/W2787894218","https://openalex.org/W2790639531","https://openalex.org/W2805089815","https://openalex.org/W2890857765","https://openalex.org/W2899649350","https://openalex.org/W2902537416","https://openalex.org/W2903950532","https://openalex.org/W2911964244","https://openalex.org/W2914959816","https://openalex.org/W2952164580","https://openalex.org/W2993383518","https://openalex.org/W4210736171","https://openalex.org/W6640462745","https://openalex.org/W6750542860"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W4233347783","https://openalex.org/W2910064364","https://openalex.org/W4255224757","https://openalex.org/W2499527417"],"abstract_inverted_index":{"Background.":[0],"Electronic":[1],"Health":[2],"Records":[3],"(EHR)":[4],"play":[5],"an":[6,108],"important":[7],"role":[8],"for":[9,208,226],"the":[10,17,36,80,98,103,114,134,141,147,158,172,184,188,228],"redefinition":[11],"of":[12,16,21,38,63,74,117,149,182,190,210,241,248,258,262,304],"phenotypes":[13,87],"in":[14,79,102,232,269,289],"view":[15],"wealth":[18],"and":[19,97,127,145,199,217,260,286],"heterogeneity":[20],"information":[22,154],"now":[23],"available":[24],"from":[25,298],"disparate":[26],"data":[27,100,136],"sources.":[28],"A":[29,152],"recent":[30],"cross-sectional":[31],"retrospective":[32],"study":[33,82],"has":[34],"described":[35],"potential":[37],"EHR":[39,86,118,150,229,237,270,305],"toward":[40],"type":[41],"2":[42],"diabetes":[43],"mellitus":[44],"(T2D)":[45],"screening":[46],"when":[47],"ad":[48],"hoc":[49],"models":[50],"are":[51,238,250],"used.":[52],"About":[53],"10,000":[54],"US":[55,95],"patients":[56,96,249],"have":[57,83,273],"been":[58],"analyzed":[59],"through":[60,244],"a":[61,71,179,224,233,300],"variety":[62],"inference":[64,110,159],"techniques":[65],"applied":[66],"to":[67,140,162,166,284,291],"all":[68],"records":[69],"with":[70,178,296],"variable":[72],"degree":[73,181],"completeness.":[75],"The":[76,129,280],"analyses":[77],"conducted":[78],"reference":[81],"indicated":[84],"that":[85,112],"significantly":[88],"improved":[89],"T2D":[90,99,169,197,214,278,292],"detection.":[91],"Methods.":[92],"With":[93],"these":[94,254],"evidenced":[101],"above":[104],"study,":[105],"we":[106],"propose":[107],"integrative":[109],"approach":[111,222],"leverages":[113],"prediction":[115,264],"power":[116],"features":[119,170],"selected":[120],"by":[121],"two":[122,211],"well-known":[123],"methods,":[124],"Random":[125],"Forests":[126],"Lasso.":[128],"goal":[130],"is":[131,157],"twofold:":[132],"reducing":[133],"big":[135],"redundancies":[137],"potentially":[138],"harmful":[139],"predictive":[142],"learning":[143],"task":[144],"exploiting":[146],"inter-connectivity":[148],"features.":[151,306],"mutual":[153],"(MI)":[155],"network":[156],"tool":[160],"used":[161],"identify":[163],"communities":[164,185],"useful":[165],"prioritize":[167],"significant":[168],"underlying":[171],"similarity":[173],"between":[174],"patients.":[175],"Results.":[176],"Endowed":[177],"different":[180],"granularity,":[183],"detected":[186],"after":[187],"application":[189],"both":[191,256],"methods":[192,265],"were":[193],"centered":[194],"especially":[195],"on":[196],"comorbidities":[198,285],"risk":[200,263,287],"factors.":[201],"As":[202],"such,":[203],"they":[204],"appear":[205],"very":[206],"relevant":[207],"assessment":[209],"main":[212],"issues,":[213],"disease":[215,235],"burden":[216],"prevention.":[218],"Conclusions.":[219],"Our":[220],"analytical":[221],"offers":[223],"solution":[225],"managing":[227],"scale":[230],"factor":[231],"complex":[234],"context.":[236],"rich":[239],"sources":[240],"phenotypic":[242],"diversity":[243],"which":[245],"novel":[246],"stratifications":[247],"expected.":[251],"To":[252],"enable":[253],"results,":[255],"pre-screening":[257],"variables":[259],"calibration":[261],"become":[266],"necessary":[267],"steps":[268],"analyses.":[271],"We":[272],"presented":[274],"networks":[275],"identifying":[276],"major":[277],"communities.":[279],"specific":[281],"significance":[282],"assigned":[283],"factors":[288],"relation":[290],"can":[293],"be":[294],"inferred":[295],"accuracy":[297],"just":[299],"suitably":[301],"reduced":[302],"number":[303]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-05-16T08:24:45.110214","created_date":"2025-10-10T00:00:00"}