{"id":"https://openalex.org/W4288421349","doi":"https://doi.org/10.1145/3535508.3545561","title":"Predicting acute events using the movement patterns of older adults","display_name":"Predicting acute events using the movement patterns of older adults","publication_year":2022,"publication_date":"2022-07-28","ids":{"openalex":"https://openalex.org/W4288421349","doi":"https://doi.org/10.1145/3535508.3545561"},"language":"en","primary_location":{"id":"doi:10.1145/3535508.3545561","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3535508.3545561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","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/A5025756618","display_name":"Ramin Ramazi","orcid":null},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramin Ramazi","raw_affiliation_strings":["University of Delaware"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Delaware","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072266451","display_name":"Mary Elizabeth Bowen","orcid":"https://orcid.org/0000-0003-1806-0194"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mary Elizabeth Bowen","raw_affiliation_strings":["University of Delaware"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Delaware","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044208559","display_name":"Rahmatollah Beheshti","orcid":"https://orcid.org/0000-0001-8912-3063"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rahmatollah Beheshti","raw_affiliation_strings":["University of Delaware"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Delaware","institution_ids":["https://openalex.org/I86501945"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08345144,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"144","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9961000084877014,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.98580002784729,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/cluster-analysis","display_name":"Cluster analysis","score":0.7099807262420654},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7005069851875305},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5065757036209106},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4813700318336487},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45503661036491394},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4518461227416992},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.4129018485546112},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41075676679611206},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12007617950439453}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7099807262420654},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7005069851875305},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5065757036209106},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4813700318336487},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45503661036491394},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4518461227416992},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.4129018485546112},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41075676679611206},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12007617950439453},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3535508.3545561","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3535508.3545561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3592879053","display_name":null,"funder_award_id":"3P20GM103446","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W69342273","https://openalex.org/W89608058","https://openalex.org/W1784044093","https://openalex.org/W1828385005","https://openalex.org/W1936527623","https://openalex.org/W1967131988","https://openalex.org/W1987971958","https://openalex.org/W1995160531","https://openalex.org/W2001215426","https://openalex.org/W2002262084","https://openalex.org/W2004319861","https://openalex.org/W2012775342","https://openalex.org/W2027412674","https://openalex.org/W2035181707","https://openalex.org/W2057360761","https://openalex.org/W2058308089","https://openalex.org/W2082035758","https://openalex.org/W2084616221","https://openalex.org/W2092877006","https://openalex.org/W2097747115","https://openalex.org/W2122200441","https://openalex.org/W2150798635","https://openalex.org/W2166386587","https://openalex.org/W2342783192","https://openalex.org/W2536891437","https://openalex.org/W2586341316","https://openalex.org/W2613395669","https://openalex.org/W2741943936","https://openalex.org/W2767807103","https://openalex.org/W2774829855","https://openalex.org/W2795446003","https://openalex.org/W2911276407","https://openalex.org/W2919001929","https://openalex.org/W2963761396","https://openalex.org/W2971413519","https://openalex.org/W2997194371","https://openalex.org/W3004946360","https://openalex.org/W3101709902","https://openalex.org/W4221167823"],"related_works":["https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2132641928","https://openalex.org/W4310225030","https://openalex.org/W2090259340","https://openalex.org/W1926736923","https://openalex.org/W2158836806","https://openalex.org/W2393816671","https://openalex.org/W2083665254","https://openalex.org/W2942177010"],"abstract_inverted_index":{"Timely":[0],"identification":[1],"of":[2,8,24,37,44,60,130,209],"individuals":[3],"with":[4,83,141],"a":[5,104,169],"high":[6],"risk":[7,208],"imminent":[9],"acute":[10,61,212],"events":[11,26,62],"in":[12,18,49],"long-term":[13],"care":[14],"facilities":[15],"can":[16,92,192,204],"aid":[17],"reducing":[19],"the":[20,41,142,207],"frequency":[21],"or":[22,157],"severity":[23],"such":[25,63,78,99],"and":[27,46,58,67,144,181],"lead":[28],"to":[29,161,195],"safer":[30],"residential":[31],"environments.":[32],"Specifically,":[33],"an":[34,122],"interval-based":[35],"classification":[36],"mobility":[38,80],"behavior":[39,81],"(i.e.,":[40],"real-time":[42],"pattern":[43],"walking":[45],"physical":[47],"activities":[48],"older":[50],"adults)":[51],"has":[52,72],"been":[53,74],"used":[54,194],"for":[55,126],"early":[56],"recognition":[57],"prevention":[59],"as":[64,89,107],"falls,":[65],"delirium,":[66],"urinary":[68],"tract":[69],"infections.":[70],"It":[71],"also":[73],"shown":[75],"that":[76,149,188,203],"supplementing":[77],"temporal":[79],"data":[82,102,132,177],"static":[84,143,180],"cognitive":[85],"condition":[86],"information":[87],"(such":[88],"test":[90],"scores)":[91],"yield":[93],"better":[94],"prediction":[95],"results.":[96],"However,":[97],"classifying":[98,127],"multi-modal":[100,131],"(static+time-series)":[101],"is":[103],"challenging":[105],"task":[106],"it":[108],"requires":[109],"simultaneously":[110],"taking":[111],"different":[112],"similarity":[113],"relationships":[114],"into":[115,199],"account.":[116],"In":[117],"this":[118,128],"work,":[119],"we":[120,186],"present":[121],"unsupervised":[123],"clustering":[124,164,175,190],"technique":[125],"type":[129],"points":[133],"via":[134],"jointly":[135],"optimizing":[136],"separate":[137],"objective":[138],"functions":[139],"associated":[140],"time-series":[145,176,182],"parts.":[146],"We":[147],"show":[148,187],"our":[150,189],"customized":[151],"deep":[152],"learning":[153],"pipeline":[154],"achieves":[155],"competitive":[156],"superior":[158],"results":[159],"compared":[160],"several":[162],"recent":[163],"baselines":[165],"when":[166],"studied":[167],"on":[168],"few":[170],"generic":[171],"tasks":[172],"aiming":[173],"at":[174],"using":[178],"both":[179],"data.":[183],"Following":[184],"this,":[185],"model":[191],"be":[193],"cluster":[196],"movement":[197],"patterns":[198],"clinically":[200],"meaningful":[201],"clusters":[202],"effectively":[205],"capture":[206],"near":[210],"future":[211],"events.":[213]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}