{"id":"https://openalex.org/W2598433306","doi":"https://doi.org/10.1145/3233547.3233553","title":"Cohesion-driven Online Actor-Critic Reinforcement Learning for mHealth Intervention","display_name":"Cohesion-driven Online Actor-Critic Reinforcement Learning for mHealth Intervention","publication_year":2018,"publication_date":"2018-08-15","ids":{"openalex":"https://openalex.org/W2598433306","doi":"https://doi.org/10.1145/3233547.3233553","mag":"2598433306"},"language":"en","primary_location":{"id":"doi:10.1145/3233547.3233553","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3233547.3233553","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3233547.3233553","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3233547.3233553","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103796253","display_name":"Feiyun Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feiyun Zhu","raw_affiliation_strings":["The University of Texas at Arlington, Arlington, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Arlington, Arlington, TX, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050868515","display_name":"Peng Liao","orcid":"https://orcid.org/0000-0002-8409-5574"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Liao","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102586120","display_name":"Xinliang Zhu","orcid":"https://orcid.org/0000-0002-4544-2078"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinliang Zhu","raw_affiliation_strings":["The University of Texas at Arlington, Arlington, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Arlington, Arlington, TX, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075245264","display_name":"Jiawen Yao","orcid":"https://orcid.org/0000-0001-7429-2964"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiawen Yao","raw_affiliation_strings":["The University of Texas at Arlington, Arlington, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Arlington, Arlington, TX, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068865316","display_name":"Junzhou Huang","orcid":"https://orcid.org/0000-0002-9548-1227"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junzhou Huang","raw_affiliation_strings":["The University of Texas at Arlington; Tencent AI Lab, Arlington, TX, USA","The University of Texas at Arlington"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Arlington; Tencent AI Lab, Arlington, TX, USA","institution_ids":["https://openalex.org/I189196454"]},{"raw_affiliation_string":"The University of Texas at Arlington","institution_ids":["https://openalex.org/I189196454"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7538,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.87756351,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"12","issue":null,"first_page":"482","last_page":"491"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10355","display_name":"Impact of Technology on Adolescents","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10355","display_name":"Impact of Technology on Adolescents","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.979200005531311,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11519","display_name":"Digital Mental Health Interventions","score":0.928600013256073,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7320302128791809},{"id":"https://openalex.org/keywords/cohesion","display_name":"Cohesion (chemistry)","score":0.662914514541626},{"id":"https://openalex.org/keywords/mhealth","display_name":"mHealth","score":0.6596364378929138},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6434235572814941},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.442065566778183},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.4260125756263733},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4215434491634369},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3876373767852783},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.36646220088005066},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3337196707725525},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.23885560035705566},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.1016508936882019}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7320302128791809},{"id":"https://openalex.org/C104054115","wikidata":"https://www.wikidata.org/wiki/Q216828","display_name":"Cohesion (chemistry)","level":2,"score":0.662914514541626},{"id":"https://openalex.org/C2779363104","wikidata":"https://www.wikidata.org/wiki/Q17069079","display_name":"mHealth","level":3,"score":0.6596364378929138},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6434235572814941},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.442065566778183},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.4260125756263733},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4215434491634369},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3876373767852783},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.36646220088005066},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3337196707725525},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.23885560035705566},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.1016508936882019},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3233547.3233553","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3233547.3233553","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3233547.3233553","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3233547.3233553","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3233547.3233553","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3233547.3233553","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.699999988079071}],"awards":[{"id":"https://openalex.org/G1183982361","display_name":null,"funder_award_id":"CMMI-1434401","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"},{"id":"https://openalex.org/G3387330441","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"},{"id":"https://openalex.org/G3719915608","display_name":"III: Small: Collaborative Research: Robust Materials Genome Data Mining Framework for Prediction and Guidance of Nanoparticle Synthesis","funder_award_id":"1423056","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6105257721","display_name":"Statistics-based Optimization Methods for Adaptive Interdisciplinary Pain Management","funder_award_id":"1434401","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7372974065","display_name":"CI-P: Planning for SMART-MOVE: A Spatiotemporal Annotated Human Activity Repository for Advanced Motion Recognition and Analysis Research","funder_award_id":"1405985","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8238368010","display_name":null,"funder_award_id":"1434401","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"},{"id":"https://openalex.org/G8399341322","display_name":"RI: Small: Collaborative Research: A Topological Analysis of Uncertainly Representation in the Brain","funder_award_id":"1718853","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8434690768","display_name":"CAREER: Large Scale Learning for Complex Image-Omics Data Analytics","funder_award_id":"1553687","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2598433306.pdf","grobid_xml":"https://content.openalex.org/works/W2598433306.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1460549585","https://openalex.org/W1987543638","https://openalex.org/W1991576326","https://openalex.org/W1999912147","https://openalex.org/W2015251183","https://openalex.org/W2027316797","https://openalex.org/W2046376809","https://openalex.org/W2047665796","https://openalex.org/W2065393799","https://openalex.org/W2072758956","https://openalex.org/W2072837080","https://openalex.org/W2111492941","https://openalex.org/W2112264645","https://openalex.org/W2112420033","https://openalex.org/W2117046315","https://openalex.org/W2118665898","https://openalex.org/W2121863487","https://openalex.org/W2132914434","https://openalex.org/W2140790583","https://openalex.org/W2149571656","https://openalex.org/W2153215853","https://openalex.org/W2153771494","https://openalex.org/W2252409454","https://openalex.org/W2320605305","https://openalex.org/W2339928330","https://openalex.org/W2489708862","https://openalex.org/W2500478584","https://openalex.org/W2595142274","https://openalex.org/W2730983848","https://openalex.org/W2748653356","https://openalex.org/W2951573205","https://openalex.org/W4285719527","https://openalex.org/W4300369184","https://openalex.org/W6691666647"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119","https://openalex.org/W4285226279","https://openalex.org/W4288019534"],"abstract_inverted_index":{"In":[0,118],"the":[1,4,49,80,96,149,158,165,201,205,212,226,232,245,252,269,280,288],"wake":[2],"of":[3,7,91,160,217,235,277],"vast":[5],"population":[6],"smart":[8],"device":[9],"users":[10,50,59,112,145],"worldwide,":[11],"mobile":[12],"health":[13,36],"(mHealth)":[14],"technologies":[15],"are":[16,28,51],"hopeful":[17],"to":[18,30,38,78,85,114,137,140,146,188,199,231,261],"generate":[19],"positive":[20],"and":[21,34,60,110,172,222],"wide":[22],"influence":[23],"on":[24,268],"people's":[25],"health.":[26],"They":[27,54],"able":[29],"provide":[31],"flexible,":[32],"affordable":[33],"portable":[35],"guides":[37],"devise":[39],"users.":[40],"Current":[41],"online":[42,82,167,247],"decision-making":[43],"methods":[44,254,283],"for":[45,65,70,132,170,244],"mHealth":[46,171],"assume":[47],"that":[48,88,98,273],"completely":[52],"heterogeneous.":[53],"share":[55,141],"no":[56],"information":[57,142,152],"among":[58,143],"learn":[61,200],"a":[62,99,123,196,238,275],"separate":[63,81],"policy":[64],"each":[66,71],"user.":[67],"However,":[68],"data":[69],"user":[72,100,151],"is":[73,136,164,186,220],"very":[74,223],"limited":[75,150],"in":[76,180,274],"size":[77],"support":[79],"learning,":[83],"leading":[84],"unstable":[86],"policies":[87],"contain":[89],"lots":[90],"variances.":[92],"Besides,":[93],"we":[94,121,240],"find":[95],"truth":[97],"may":[101],"be":[102,256],"similar":[103,116,144],"with":[104,195],"some,":[105],"but":[106],"not":[107],"all,":[108],"users,":[109],"connected":[111],"tend":[113],"have":[115],"behaviors.":[117],"this":[119,163],"paper,":[120],"propose":[122,241],"network":[124,174,184,202],"cohesion":[125,175,185],"constrained":[126,176],"(actor-critic)":[127,177],"Reinforcement":[128],"Learning":[129],"(RL)":[130],"method":[131,179,198],"mHealth.":[133],"The":[134,183,215],"goal":[135],"explore":[138],"how":[139],"better":[147],"convert":[148],"into":[153],"sharper":[154],"learned":[155],"policies.":[156,191],"To":[157],"best":[159],"our":[161,218],"knowledge,":[162],"first":[166,173],"actor-critic":[168],"RL":[169,178],"all":[181],"applications.":[182],"important":[187],"derive":[189],"effective":[190],"We":[192],"come":[193],"up":[194],"novel":[197],"by":[203],"using":[204],"warm":[206],"start":[207],"trajectory,":[208],"which":[209],"directly":[210],"reflects":[211],"users'":[213],"property.":[214],"optimization":[216],"model":[219],"difficult":[221],"different":[224],"from":[225,250],"general":[227],"supervised":[228],"learning":[229],"due":[230],"indirect":[233],"observation":[234],"values.":[236],"As":[237],"contribution,":[239],"two":[242,282],"algorithms":[243],"proposed":[246,253,281],"RLs.":[248],"Apart":[249],"mHealth,":[251],"can":[255],"easily":[257],"applied":[258],"or":[259],"adapted":[260],"other":[262],"health-related":[263],"tasks.":[264],"Extensive":[265],"experiment":[266],"results":[267],"HeartSteps":[270],"dataset":[271],"demonstrates":[272],"variety":[276],"parameter":[278],"settings,":[279],"obtain":[284],"obvious":[285],"improvements":[286],"over":[287],"state-of-the-art":[289],"methods.":[290]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
