{"id":"https://openalex.org/W4206501383","doi":"https://doi.org/10.1109/bigdata52589.2021.9671306","title":"TBI2Vec: Traumatic Brain Injury Smartphone Sensing using AutoEncoder Embeddings","display_name":"TBI2Vec: Traumatic Brain Injury Smartphone Sensing using AutoEncoder Embeddings","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206501383","doi":"https://doi.org/10.1109/bigdata52589.2021.9671306"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671306","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/A5112732116","display_name":"SAYALI SHELKE -","orcid":null},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sayali Shelke","raw_affiliation_strings":["Computer Science Department, Worcester Polytechnic Institute (WPI), Worcester, MA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, Worcester Polytechnic Institute (WPI), Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003809101","display_name":"Emmanuel Agu","orcid":"https://orcid.org/0000-0002-3361-4952"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Emmanuel Agu","raw_affiliation_strings":["Computer Science Department, Worcester Polytechnic Institute (WPI), Worcester, MA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, Worcester Polytechnic Institute (WPI), Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112732116"],"corresponding_institution_ids":["https://openalex.org/I107077323"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.41025641,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4770","last_page":"4779"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10645","display_name":"Cardiac Arrest and Resuscitation","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10645","display_name":"Cardiac Arrest and Resuscitation","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"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/T10416","display_name":"Traumatic Brain Injury Research","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/T13248","display_name":"Healthcare Technology and Patient Monitoring","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/autoencoder","display_name":"Autoencoder","score":0.8707882761955261},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6268701553344727},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6012529134750366},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5988128781318665},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.519556999206543},{"id":"https://openalex.org/keywords/traumatic-brain-injury","display_name":"Traumatic brain injury","score":0.5035781264305115},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4312536418437958},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41942524909973145},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.41585785150527954},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35437002778053284},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2560700476169586}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8707882761955261},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6268701553344727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6012529134750366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5988128781318665},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.519556999206543},{"id":"https://openalex.org/C2781017439","wikidata":"https://www.wikidata.org/wiki/Q1995526","display_name":"Traumatic brain injury","level":2,"score":0.5035781264305115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4312536418437958},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41942524909973145},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.41585785150527954},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35437002778053284},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2560700476169586},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671306","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/10","display_name":"Reduced inequalities","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2131496831","https://openalex.org/W2167984537","https://openalex.org/W2342935467","https://openalex.org/W2480671298","https://openalex.org/W2531023376","https://openalex.org/W2531635091","https://openalex.org/W2573003069","https://openalex.org/W2574652423","https://openalex.org/W2586762861","https://openalex.org/W2755569691","https://openalex.org/W2801625478","https://openalex.org/W2903325146","https://openalex.org/W2909777362","https://openalex.org/W2913090618","https://openalex.org/W2913495735","https://openalex.org/W2919292274","https://openalex.org/W2943952292","https://openalex.org/W2955495654","https://openalex.org/W3012762625","https://openalex.org/W3081430124","https://openalex.org/W3137981739","https://openalex.org/W4288283203","https://openalex.org/W6751493020","https://openalex.org/W6765846377"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000"],"abstract_inverted_index":{"TBI":[0,18,39,64,80,90,114,213,265],"causes":[1],"distress":[2],"to":[3,10,51,70,112,211],"millions":[4],"of":[5,63,75,141,201,231,246,254,260],"individuals":[6],"and":[7,14,78,158,195,227,256],"can":[8],"lead":[9],"significant":[11],"motor,":[12],"cognitive":[13],"emotional":[15],"deficits.":[16],"However,":[17],"patients":[19,76],"are":[20,109],"currently":[21],"assessed":[22],"infrequently":[23],"especially":[24],"in-between":[25],"scheduled":[26],"appointments.":[27],"To":[28],"facilitate":[29],"passive,":[30],"remote,":[31],"population-level":[32],"ailment":[33],"monitoring,":[34],"we":[35,92,104,122],"propose":[36],"TBI2Vec,":[37],"a":[38,66,72,84,228,249],"sensing":[40,62],"framework":[41],"that":[42,108,124],"continuously":[43],"assesses":[44],"smartphone":[45,61,88,99],"users":[46],"by":[47,185,193,262],"using":[48,129,165,180],"machine":[49],"learning":[50],"classify":[52],"smart-phone":[53],"sensor":[54,89,100],"features":[55,96,127],"encoded":[56,128],"as":[57,215,217],"autoencoder":[58,106,143],"embeddings.":[59,136],"Passive":[60],"enables":[65],"small":[67],"medical":[68],"team":[69],"monitor":[71],"large":[73],"population":[74],"passively":[77],"detect":[79,212],"early.":[81],"In":[82,119,177],"analyzing":[83],"large,":[85],"real,":[86],"crowd-sourced":[87],"dataset,":[91,164],"extracted":[93],"106":[94],"statistical":[95],"from":[97,102,116],"raw":[98],"data,":[101],"which":[103],"generated":[105],"embeddings":[107,130,144,166,181],"then":[110],"classified":[111],"distinguish":[113],"subjects":[115],"non-TBI":[117],"subjects.":[118,266],"rigorous":[120,178],"evaluations,":[121],"found":[123],"classifying":[125,173],"the":[126,132,146,174,183,188,198,225,237],"outperformed":[131],"same":[133],"models":[134,171],"without":[135],"The":[137],"dimension":[138],"reduction":[139],"process":[140],"generating":[142],"retained":[145],"most":[147],"discriminative":[148],"in-formation":[149],"while":[150],"eliminating":[151],"non-discriminative":[152],"ones,":[153],"boosting":[154],"classification":[155],"both":[156],"accuracy":[157],"generalizability.":[159],"Even":[160],"for":[161],"our":[162],"imbalanced":[163],"performed":[167,224],"better":[168],"than":[169],"baseline":[170],"in":[172],"minority":[175],"class.":[176],"evaluation,":[179],"increased":[182],"F-beta(0.5)":[184],"34-71%,":[186],"decreased":[187],"False":[189,257],"Negative":[190,251,258],"Rate":[191,252,259],"(FNR)":[192],"20-100%":[194],"significantly":[196],"reduced":[197],"cross-fold":[199],"variation":[200],"accuracies":[202],"achieved":[203,241],"during":[204],"k-fold":[205],"cross":[206],"validation.":[207],"TBI2Vec":[208,240],"was":[209],"able":[210],"occurrence":[214],"early":[216],"24":[218],"hours":[219,235],"after":[220],"injury.":[221],"Random":[222],"Forest":[223],"best":[226,238],"window":[229],"size":[230],"2":[232],"days":[233],"12":[234],"gave":[236],"results.":[239],"an":[242],"F-beta":[243],"(0.5)":[244],"score":[245],"83.0%":[247],"with":[248],"True":[250],"(TNR)":[253],"96.0%":[255],"33.0%":[261],"correctly":[263],"identifying":[264]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
