{"id":"https://openalex.org/W4415981674","doi":"https://doi.org/10.1109/ictc66702.2025.11389118","title":"Subject-Adaptive Sparse Linear Models for Interpretable Personalized Health Prediction from Multimodal Lifelog Data","display_name":"Subject-Adaptive Sparse Linear Models for Interpretable Personalized Health Prediction from Multimodal Lifelog Data","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W4415981674","doi":"https://doi.org/10.1109/ictc66702.2025.11389118"},"language":"en","primary_location":{"id":"doi:10.1109/ictc66702.2025.11389118","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc66702.2025.11389118","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.02835","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120289267","display_name":"Dohyun Bu","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dohyun Bu","raw_affiliation_strings":["KAIST,Department of Industrial &#x0026; Systems Engineering,Daejeon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST,Department of Industrial &#x0026; Systems Engineering,Daejeon,South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068690598","display_name":"Jisoo Han","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jisoo Han","raw_affiliation_strings":["Sungkyunkwan University,Department of Systems Management Engineering,Suwon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Department of Systems Management Engineering,Suwon,South Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Soohwa Kwon","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soohwa Kwon","raw_affiliation_strings":["Sungkyunkwan University,Department of Systems Management Engineering,Suwon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Department of Systems Management Engineering,Suwon,South Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111027462","display_name":"Y.T. So","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yulim So","raw_affiliation_strings":["Sungkyunkwan University,Department of Industrial Engineering,Suwon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Department of Industrial Engineering,Suwon,South Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100668168","display_name":"Jong-Seok Lee","orcid":"https://orcid.org/0000-0003-3733-3573"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jong-Seok Lee","raw_affiliation_strings":["KAIST,Department of Industrial &#x0026; Systems Engineering,Daejeon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST,Department of Industrial &#x0026; Systems Engineering,Daejeon,South Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29027917,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1687","last_page":"1692"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.7516000270843506,"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":0.7516000270843506,"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/T10316","display_name":"Sleep and related disorders","score":0.02759999968111515,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.02500000037252903,"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":0.8687999844551086},{"id":"https://openalex.org/keywords/lifelog","display_name":"Lifelog","score":0.7807000279426575},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5372999906539917},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5005000233650208},{"id":"https://openalex.org/keywords/elastic-net-regularization","display_name":"Elastic net regularization","score":0.42640000581741333},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.41499999165534973},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.38260000944137573},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.37630000710487366},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.3434000015258789},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.34119999408721924}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8687999844551086},{"id":"https://openalex.org/C176168674","wikidata":"https://www.wikidata.org/wiki/Q763835","display_name":"Lifelog","level":2,"score":0.7807000279426575},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7106000185012817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6154000163078308},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.541100025177002},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5372999906539917},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5005000233650208},{"id":"https://openalex.org/C203868755","wikidata":"https://www.wikidata.org/wiki/Q5353562","display_name":"Elastic net regularization","level":3,"score":0.42640000581741333},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.41499999165534973},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4025999903678894},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.37630000710487366},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3434000015258789},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C32220436","wikidata":"https://www.wikidata.org/wiki/Q2072214","display_name":"Personalized medicine","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C145642194","wikidata":"https://www.wikidata.org/wiki/Q870895","display_name":"Health informatics","level":3,"score":0.2881999909877777},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.27869999408721924},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C110313322","wikidata":"https://www.wikidata.org/wiki/Q7100793","display_name":"Ordinal regression","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C99656134","wikidata":"https://www.wikidata.org/wiki/Q2912993","display_name":"Ordinary least squares","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ictc66702.2025.11389118","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc66702.2025.11389118","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2510.02835","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.02835","pdf_url":"https://arxiv.org/pdf/2510.02835","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.02835","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.02835","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.02835","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.02835","pdf_url":"https://arxiv.org/pdf/2510.02835","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W4386225004","https://openalex.org/W3120440973","https://openalex.org/W4391590144","https://openalex.org/W2972181925","https://openalex.org/W3099601484","https://openalex.org/W4301800424","https://openalex.org/W1522850626","https://openalex.org/W4225329210","https://openalex.org/W3084356688","https://openalex.org/W4406354898","https://openalex.org/W4406355670","https://openalex.org/W4406355727","https://openalex.org/W4406355997","https://openalex.org/W4406356696","https://openalex.org/W2079898917","https://openalex.org/W3194878797","https://openalex.org/W2973941913","https://openalex.org/W4403398410","https://openalex.org/W3134221708","https://openalex.org/W3096977812","https://openalex.org/W4408989246","https://openalex.org/W4385461980","https://openalex.org/W4366606077","https://openalex.org/W1997855593"],"related_works":[],"abstract_inverted_index":{"Improved":[0],"prediction":[1],"of":[2,112,175],"personalized":[3,65],"health":[4,66,106],"outcomes\u2014such":[5],"as":[6],"sleep":[7],"quality":[8],"and":[9,18,28,33,99,184,191,196],"stress\u2014from":[10],"multimodal":[11],"lifelog":[12,44],"data":[13],"could":[14],"have":[15],"meaningful":[16],"clinical":[17],"practical":[19],"implications.":[20],"However,":[21],"state-of-the-art":[22],"models,":[23],"primarily":[24],"deep":[25],"neural":[26],"networks":[27],"gradient-boosted":[29],"ensembles,":[30],"sacrifice":[31],"interpretability":[32],"fail":[34],"to":[35,95,122,173],"adequately":[36],"address":[37],"the":[38,52,153,165],"significant":[39],"inter-individual":[40],"variability":[41],"inherent":[42],"in":[43],"data.":[45],"To":[46],"overcome":[47],"these":[48],"challenges,":[49],"we":[50,115],"propose":[51],"Subject-Adaptive":[53],"Sparse":[54],"Linear":[55],"(SASL)":[56],"framework,":[57],"an":[58,85],"interpretable":[59],"modeling":[60],"approach":[61,119],"explicitly":[62],"designed":[63,121],"for":[64,127,194],"prediction.":[67],"SASL":[68,134],"integrates":[69],"ordinary":[70],"least":[71],"squares":[72],"regression":[73],"with":[74,180],"subject-specific":[75],"interactions,":[76],"systematically":[77],"distinguishing":[78],"global":[79],"from":[80,138,161],"individual-level":[81],"effects.":[82],"We":[83],"employ":[84],"iterative":[86],"backward":[87],"feature":[88],"elimination":[89],"method":[90],"based":[91],"on":[92,152],"nested":[93],"F-tests":[94],"construct":[96],"a":[97,117],"sparse":[98],"statistically":[100],"robust":[101],"model.":[102],"Additionally,":[103],"recognizing":[104],"that":[105,164,174],"outcomes":[107],"often":[108],"represent":[109],"discretized":[110],"versions":[111],"continuous":[113],"processes,":[114],"develop":[116],"regression-then-thresholding":[118],"specifically":[120],"maximize":[123],"macro-averaged":[124],"F1":[125],"scores":[126],"ordinal":[128],"targets.":[129],"For":[130],"intrinsically":[131],"challenging":[132],"predictions,":[133],"selectively":[135],"incorporates":[136],"outputs":[137],"compact":[139],"LightGBM":[140],"models":[141],"through":[142],"confidence-based":[143],"gating,":[144],"enhancing":[145],"accuracy":[146],"without":[147],"compromising":[148],"interpretability.":[149],"Evaluations":[150],"conducted":[151],"CH-2025":[154],"dataset\u2014which":[155],"comprises":[156],"roughly":[157],"450":[158],"daily":[159],"observations":[160],"ten":[162],"subjects\u2014demonstrate":[163],"hybrid":[166],"SASL-LightGBM":[167],"framework":[168],"achieves":[169],"predictive":[170],"performance":[171],"comparable":[172],"sophisticated":[176],"black\u2013box":[177],"methods,":[178],"but":[179],"significantly":[181],"fewer":[182],"parameters":[183],"substantially":[185],"greater":[186],"transparency,":[187],"thus":[188],"providing":[189],"clear":[190],"actionable":[192],"insights":[193],"clinicians":[195],"practitioners.":[197]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
