{"id":"https://openalex.org/W7128160755","doi":"https://doi.org/10.1007/s11042-026-21240-7","title":"D.R.E.A.M: diabetes risk via explainable AI modeling","display_name":"D.R.E.A.M: diabetes risk via explainable AI modeling","publication_year":2026,"publication_date":"2026-02-06","ids":{"openalex":"https://openalex.org/W7128160755","doi":"https://doi.org/10.1007/s11042-026-21240-7"},"language":"en","primary_location":{"id":"doi:10.1007/s11042-026-21240-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-026-21240-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11042-026-21240-7.pdf","source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11042-026-21240-7.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125276274","display_name":"Domenico Rossi","orcid":null},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Domenico Rossi","raw_affiliation_strings":["Computer Science, University of Salerno, Via Giovanni Paolo II, Fisciano, 84084, Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, University of Salerno, Via Giovanni Paolo II, Fisciano, 84084, Italia","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040887693","display_name":"Alessia Auriemma Citarella","orcid":"https://orcid.org/0000-0002-6525-0217"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alessia Auriemma Citarella","raw_affiliation_strings":["Computer Science, University of Salerno, Via Giovanni Paolo II, Fisciano, 84084, Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, University of Salerno, Via Giovanni Paolo II, Fisciano, 84084, Italia","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044061604","display_name":"Fabiola De Marco","orcid":"https://orcid.org/0000-0003-4285-9502"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fabiola De Marco","raw_affiliation_strings":["Computer Science, University of Salerno, Via Giovanni Paolo II, Fisciano, 84084, Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, University of Salerno, Via Giovanni Paolo II, Fisciano, 84084, Italia","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077768428","display_name":"Luigi Di Biasi","orcid":"https://orcid.org/0000-0002-9583-6681"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luigi Di Biasi","raw_affiliation_strings":["Computer Science, University of Salerno, Via Giovanni Paolo II, Fisciano, 84084, Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, University of Salerno, Via Giovanni Paolo II, Fisciano, 84084, Italia","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104126000","display_name":"Huiru Jane Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Huiru Zheng","raw_affiliation_strings":["School of Computing, Ulster University, York Street, Belfast, BT15 1ED, UK"],"raw_orcid":"https://orcid.org/0000-0001-7648-8709","affiliations":[{"raw_affiliation_string":"School of Computing, Ulster University, York Street, Belfast, BT15 1ED, UK","institution_ids":["https://openalex.org/I138801177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074856207","display_name":"Genoveffa Tortora","orcid":"https://orcid.org/0000-0003-4765-8371"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Genoveffa Tortora","raw_affiliation_strings":["Computer Science, University of Salerno, Via Giovanni Paolo II, Fisciano, 84084, Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, University of Salerno, Via Giovanni Paolo II, Fisciano, 84084, Italia","institution_ids":["https://openalex.org/I131729948"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5104126000"],"corresponding_institution_ids":["https://openalex.org/I138801177"],"apc_list":{"value":3190,"currency":"USD","value_usd":3190},"apc_paid":{"value":3190,"currency":"USD","value_usd":3190},"fwci":14.359,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.9813013,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"85","issue":"2","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.16699999570846558,"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":0.16699999570846558,"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/T10673","display_name":"Gestational Diabetes Research and Management","score":0.15860000252723694,"subfield":{"id":"https://openalex.org/subfields/2729","display_name":"Obstetrics and Gynecology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.1160999983549118,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9039999842643738},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.582099974155426},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5602999925613403},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4156000018119812},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3871000111103058},{"id":"https://openalex.org/keywords/personalized-medicine","display_name":"Personalized medicine","score":0.36730000376701355},{"id":"https://openalex.org/keywords/gestational-diabetes","display_name":"Gestational diabetes","score":0.365200012922287},{"id":"https://openalex.org/keywords/risk-assessment","display_name":"Risk assessment","score":0.3386000096797943},{"id":"https://openalex.org/keywords/decision-support-system","display_name":"Decision support system","score":0.3382999897003174}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9039999842643738},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7384999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6322000026702881},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.582099974155426},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5602999925613403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5552999973297119},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4156000018119812},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C32220436","wikidata":"https://www.wikidata.org/wiki/Q2072214","display_name":"Personalized medicine","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C2779434492","wikidata":"https://www.wikidata.org/wiki/Q126691","display_name":"Gestational diabetes","level":4,"score":0.365200012922287},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.32089999318122864},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2824000120162964},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C63527458","wikidata":"https://www.wikidata.org/wiki/Q5133829","display_name":"Clinical decision support system","level":3,"score":0.2712000012397766},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.2606000006198883},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s11042-026-21240-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-026-21240-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11042-026-21240-7.pdf","source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:publications/f3c81c0d-0df7-46d7-af2c-c26bb97346e8","is_oa":true,"landing_page_url":"https://pure.ulster.ac.uk/en/publications/f3c81c0d-0df7-46d7-af2c-c26bb97346e8","pdf_url":null,"source":{"id":"https://openalex.org/S4306402454","display_name":"Ulster University Research Portal (Ulster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138801177","host_organization_name":"University of Ulster","host_organization_lineage":["https://openalex.org/I138801177"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Rossi, D, Auriemma Citarells, A, De Marco, F, Di Biasi, L, Zheng, H & Tortora, G 2026, 'D.R.E.A.M: diabetes risk via explainable AI modeling', Multimedia Tools and Applications, vol. 85, 145, pp. 1-12. https://doi.org/10.1007/s11042-026-21240-7","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s11042-026-21240-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-026-21240-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11042-026-21240-7.pdf","source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2542593443","display_name":null,"funder_award_id":"PNC 0000001","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7128160755.pdf","grobid_xml":"https://content.openalex.org/works/W7128160755.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1966716734","https://openalex.org/W2038961236","https://openalex.org/W2096949413","https://openalex.org/W2145317285","https://openalex.org/W2146877877","https://openalex.org/W2261059368","https://openalex.org/W2331274940","https://openalex.org/W2466578202","https://openalex.org/W2807027008","https://openalex.org/W3194043032","https://openalex.org/W4221125739","https://openalex.org/W4392235735","https://openalex.org/W4401716188","https://openalex.org/W4404490147","https://openalex.org/W4404591194","https://openalex.org/W4406254167","https://openalex.org/W4406259705","https://openalex.org/W4406846918","https://openalex.org/W4407864206","https://openalex.org/W4408398701","https://openalex.org/W4410337160","https://openalex.org/W4410564705","https://openalex.org/W4412590365","https://openalex.org/W4412836678"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Most":[1],"machine":[2],"learning":[3],"models":[4,90,127],"for":[5,34,49],"diabetes":[6,52,77],"prediction":[7,56],"rely":[8],"on":[9],"small,":[10],"homogeneous":[11],"datasets":[12,69],"and":[13,29,71,84,87,123,135,157,166,175,210,225],"fixed":[14],"thresholds,":[15],"producing":[16],"binary":[17],"outputs":[18],"with":[19,62,139],"limited":[20],"clinical":[21],"utility.":[22],"These":[23],"approaches":[24],"lack":[25],"generalizability,":[26],"probabilistic":[27,206],"awareness,":[28],"interpretability,":[30],"which":[31],"are":[32,97],"essential":[33],"real-world":[35],"healthcare":[36],"adoption.":[37],"We":[38],"present":[39],"Diabetes":[40],"Risk":[41],"via":[42,179],"Explainable":[43],"AI":[44],"Modeling":[45],"(D.R.E.A.M.),":[46],"a":[47,180,219,223],"framework":[48],"Type":[50],"2":[51],"mellitus":[53],"(T2DM)":[54],"risk":[55,188],"that":[57],"delivers":[58],"continuous,":[59],"calibrated":[60,205],"probabilities":[61],"transparent":[63,224],"explanations.":[64],"D.R.E.A.M.":[65,184,197],"integrates":[66],"two":[67],"complementary":[68],"(PIMA":[70],"BRFSS":[72],"2015)":[73],"after":[74],"excluding":[75],"gestational":[76],"cases,":[78],"applies":[79],"clinically":[80,108,226],"guided":[81],"feature":[82],"engineering":[83],"class":[85],"balancing,":[86],"trains":[88],"ensemble":[89],"(Random":[91],"Forest,":[92],"XGBoost,":[93],"LightGBM).":[94],"Decision":[95],"thresholds":[96],"optimized":[98,153],"using":[99],"precision\u2013recall":[100],"curve":[101],"analysis":[102],"rather":[103],"than":[104],"default":[105],"cutoffs,":[106],"enabling":[107],"meaningful":[109],"stratification.":[110],"Model":[111],"interpretability":[112],"is":[113],"achieved":[114,128],"through":[115],"SHapley":[116],"Additive":[117],"exPlanations":[118],"(SHAP),":[119],"providing":[120],"both":[121,164],"global":[122],"patient-level":[124],"insights.":[125],"All":[126],"Area":[129],"Under":[130],"the":[131,143,161],"Curves":[132],"above":[133],"0.83":[134],"F1-scores":[136],"of":[137,146,155,163],"0.78,":[138],"Random":[140],"Forest":[141],"offering":[142],"best":[144],"balance":[145],"sensitivity":[147],"(recall":[148],"=":[149],"0.89":[150],"at":[151],"an":[152],"threshold":[154],"0.389)":[156],"interpretability.":[158],"SHAP":[159],"confirmed":[160],"contribution":[162],"physiological":[165],"behavioral":[167],"factors,":[168],"including":[169],"glucose,":[170],"BMI,":[171],"blood":[172],"pressure,":[173],"cholesterol,":[174],"physical":[176],"activity.":[177],"Accessible":[178],"lightweight":[181],"web":[182],"interface,":[183],"provides":[185],"real-time,":[186],"explainable":[187],"scores":[189],"to":[190],"support":[191,229],"personalized":[192],"preventive":[193],"strategies.":[194],"In":[195],"summary,":[196],"advances":[198],"beyond":[199],"conventional":[200],"post-hoc":[201],"explainability":[202],"by":[203],"integrating":[204],"predictions,":[207],"PRC-based":[208],"thresholding,":[209],"direct":[211],"clinician-facing":[212],"deployment.":[213],"This":[214],"combination":[215],"transforms":[216],"it":[217],"from":[218],"research":[220],"prototype":[221],"into":[222],"actionable":[227],"decision":[228],"system.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-02-07T00:00:00"}