{"id":"https://openalex.org/W4206027981","doi":"https://doi.org/10.1109/bigdata52589.2021.9672052","title":"Demographic Data-driven Deprivation Index for Predicting Chronic Diseases","display_name":"Demographic Data-driven Deprivation Index for Predicting Chronic Diseases","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206027981","doi":"https://doi.org/10.1109/bigdata52589.2021.9672052"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9672052","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9672052","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","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/A5110835135","display_name":"Olugbenga Iyiola","orcid":null},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Olugbenga Iyiola","raw_affiliation_strings":["Department of Computer Science, The University of Texas at El Paso, El Paso, TX"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Texas at El Paso, El Paso, TX","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056726740","display_name":"Monika Akbar","orcid":"https://orcid.org/0000-0002-9402-5799"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Monika Akbar","raw_affiliation_strings":["Department of Computer Science, The University of Texas at El Paso, El Paso, TX"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Texas at El Paso, El Paso, TX","institution_ids":["https://openalex.org/I164936912"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5110835135"],"corresponding_institution_ids":["https://openalex.org/I164936912"],"apc_list":null,"apc_paid":null,"fwci":0.3712,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.56565657,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9955000281333923,"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"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9955000281333923,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9768999814987183,"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/T10027","display_name":"Diabetes, Cardiovascular Risks, and Lipoproteins","score":0.9656999707221985,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/chronic-disease","display_name":"Chronic disease","score":0.6714162826538086},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"Diabetes mellitus","score":0.5891956686973572},{"id":"https://openalex.org/keywords/census","display_name":"Census","score":0.5803964138031006},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.5496857166290283},{"id":"https://openalex.org/keywords/demographics","display_name":"Demographics","score":0.5314674973487854},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.48767244815826416},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"Blood pressure","score":0.4780367314815521},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.4727122485637665},{"id":"https://openalex.org/keywords/non-communicable-disease","display_name":"Non-communicable disease","score":0.4118722975254059},{"id":"https://openalex.org/keywords/gerontology","display_name":"Gerontology","score":0.4087650775909424},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.3835815489292145},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.32943135499954224},{"id":"https://openalex.org/keywords/environmental-health","display_name":"Environmental health","score":0.30075603723526},{"id":"https://openalex.org/keywords/intensive-care-medicine","display_name":"Intensive care medicine","score":0.1969059705734253},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.19259098172187805},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.16500955820083618}],"concepts":[{"id":"https://openalex.org/C2987552334","wikidata":"https://www.wikidata.org/wiki/Q383126","display_name":"Chronic disease","level":2,"score":0.6714162826538086},{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.5891956686973572},{"id":"https://openalex.org/C52130261","wikidata":"https://www.wikidata.org/wiki/Q39825","display_name":"Census","level":3,"score":0.5803964138031006},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.5496857166290283},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.5314674973487854},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.48767244815826416},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.4780367314815521},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.4727122485637665},{"id":"https://openalex.org/C2776577793","wikidata":"https://www.wikidata.org/wiki/Q3137154","display_name":"Non-communicable disease","level":3,"score":0.4118722975254059},{"id":"https://openalex.org/C74909509","wikidata":"https://www.wikidata.org/wiki/Q10387","display_name":"Gerontology","level":1,"score":0.4087650775909424},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.3835815489292145},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32943135499954224},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.30075603723526},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.1969059705734253},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.19259098172187805},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.16500955820083618},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C134018914","wikidata":"https://www.wikidata.org/wiki/Q162606","display_name":"Endocrinology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9672052","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9672052","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","score":0.6700000166893005,"display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1589843265","https://openalex.org/W1740778703","https://openalex.org/W2043557689","https://openalex.org/W2148143831","https://openalex.org/W2154442536","https://openalex.org/W2163215699","https://openalex.org/W2230691757","https://openalex.org/W2289299143","https://openalex.org/W2342005992","https://openalex.org/W2419498871","https://openalex.org/W2510487214","https://openalex.org/W2738537675","https://openalex.org/W2791077003","https://openalex.org/W2796884049","https://openalex.org/W2889839284","https://openalex.org/W2971096721","https://openalex.org/W3021925134","https://openalex.org/W4213099427","https://openalex.org/W6696279670","https://openalex.org/W6717276941","https://openalex.org/W6742136276"],"related_works":["https://openalex.org/W3029811765","https://openalex.org/W4285496533","https://openalex.org/W2366318808","https://openalex.org/W4309839579","https://openalex.org/W2368290885","https://openalex.org/W2407101774","https://openalex.org/W2373362895","https://openalex.org/W3117676362","https://openalex.org/W2080284794","https://openalex.org/W2388584348"],"abstract_inverted_index":{"Researchers":[0],"have":[1],"worked":[2],"on":[3],"modeling":[4,102],"and":[5,16,37,52,103,109],"predicting":[6,104,139],"the":[7,62,80,93],"likelihood":[8],"of":[9,29,56,79,82,95],"developing":[10],"chronic":[11,87,106,140],"diseases,":[12],"such":[13,72],"as":[14,133],"diabetes":[15,108],"high":[17,110],"blood":[18,25,111],"pressure,":[19],"using":[20,96],"medical":[21,69,97],"data":[22,73,100,131],"(e.g.,":[23,40],"heart-rate,":[24],"sugar).":[26],"However,":[27],"many":[28],"these":[30],"diseases":[31],"demonstrate":[32],"strong":[33],"links":[34],"with":[35],"demographics":[36],"socio-economic":[38,53],"status":[39],"race,":[41],"gender,":[42],"income).":[43],"It":[44],"is":[45],"also":[46],"less":[47],"time-consuming":[48],"to":[49,67,85,119],"retrieve":[50],"demographic":[51,99,125,130],"data,":[54],"some":[55],"which":[57],"are":[58,132],"publicly":[59],"available":[60],"through":[61],"U.S.":[63],"Census":[64],"Bureau,":[65],"than":[66],"conduct":[68],"tests.":[70],"Hence,":[71],"can":[74],"give":[75],"a":[76,83,86],"quicker":[77],"estimate":[78],"susceptibility":[81],"person":[84],"disease.In":[88],"this":[89],"work,":[90],"we":[91],"study":[92],"effect":[94],"vs.":[98],"for":[101,138],"two":[105],"diseases:":[107],"pressure.":[112],"We":[113],"proposed":[114],"an":[115],"updated":[116],"deprivation":[117],"index":[118],"build":[120],"disease":[121],"models":[122],"that":[123],"consider":[124],"data.":[126],"Our":[127],"results":[128],"indicate":[129],"good":[134],"or":[135],"better":[136],"indicators":[137],"diseases.":[141]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}