{"id":"https://openalex.org/W4387560685","doi":"https://doi.org/10.48550/arxiv.2310.06075","title":"Pain Forecasting using Self-supervised Learning and Patient Phenotyping: An attempt to prevent Opioid Addiction","display_name":"Pain Forecasting using Self-supervised Learning and Patient Phenotyping: An attempt to prevent Opioid Addiction","publication_year":2023,"publication_date":"2023-10-09","ids":{"openalex":"https://openalex.org/W4387560685","doi":"https://doi.org/10.48550/arxiv.2310.06075"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2310.06075","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.06075","pdf_url":"https://arxiv.org/pdf/2310.06075","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2310.06075","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060884184","display_name":"Swati Padhee","orcid":"https://orcid.org/0000-0001-5461-601X","observed_orcids":["https://orcid.org/0000-0001-5461-601X"]},"institutions":[{"id":"https://openalex.org/I19648265","display_name":"Wright State University","ror":"https://ror.org/04qk6pt94","country_code":"US","type":"education","lineage":["https://openalex.org/I19648265"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Padhee, Swati","raw_affiliation_strings":["Computer Science and Engineering , Wright State University , Dayton , OH , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering , Wright State University , Dayton , OH , USA","institution_ids":["https://openalex.org/I19648265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084532918","display_name":"Tanvi Banerjee","orcid":"https://orcid.org/0000-0002-9794-3755","observed_orcids":["https://orcid.org/0000-0002-9794-3755"]},"institutions":[{"id":"https://openalex.org/I19648265","display_name":"Wright State University","ror":"https://ror.org/04qk6pt94","country_code":"US","type":"education","lineage":["https://openalex.org/I19648265"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Banerjee, Tanvi","raw_affiliation_strings":["Computer Science and Engineering , Wright State University , Dayton , OH , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering , Wright State University , Dayton , OH , USA","institution_ids":["https://openalex.org/I19648265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086653394","display_name":"Daniel M. Abrams","orcid":"https://orcid.org/0000-0002-6015-8358","observed_orcids":["https://orcid.org/0000-0002-6015-8358"]},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abrams, Daniel M.","raw_affiliation_strings":["Engineering Science and Applied Mathematics , Northwestern University , Evanston , IL , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Engineering Science and Applied Mathematics , Northwestern University , Evanston , IL , USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083173795","display_name":"Nirmish Shah","orcid":"https://orcid.org/0000-0002-7506-0935","observed_orcids":["https://orcid.org/0000-0002-7506-0935"]},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shah, Nirmish","raw_affiliation_strings":["Department of Medicine , Duke University , Durham , NC , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Medicine , Duke University , Durham , NC , USA","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.5785999894142151,"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":0.5785999894142151,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.28780001401901245,"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/T10554","display_name":"Hemoglobinopathies and Related Disorders","score":0.06530000269412994,"subfield":{"id":"https://openalex.org/subfields/2716","display_name":"Genetics"},"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/sickle-cell-disease","display_name":"sickle cell disease","score":0.9570000171661377},{"id":"https://openalex.org/keywords/opioid-addiction-prevention","display_name":"opioid addiction prevention","score":0.7950000166893005},{"id":"https://openalex.org/keywords/self-supervised-learning","display_name":"self-supervised learning","score":0.984000027179718},{"id":"https://openalex.org/keywords/patient-phenotyping","display_name":"patient phenotyping","score":0.9240000247955322},{"id":"https://openalex.org/keywords/time-series-clustering","display_name":"time series clustering","score":0.9509999752044678},{"id":"https://openalex.org/keywords/patient-subgroups","display_name":"patient subgroups","score":0.6079999804496765},{"id":"https://openalex.org/keywords/clinical-decision-support","display_name":"clinical decision support","score":0.7400000095367432},{"id":"https://openalex.org/keywords/chronic-pain-management","display_name":"chronic pain management","score":0.6010000109672546}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.624911367893219},{"id":"https://openalex.org/C48856860","wikidata":"https://www.wikidata.org/wiki/Q12029","display_name":"Addiction","level":2,"score":0.5752159357070923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5388661623001099},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4960244596004486},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4448374807834625},{"id":"https://openalex.org/C2781063702","wikidata":"https://www.wikidata.org/wiki/Q427523","display_name":"Opioid","level":3,"score":0.44328999519348145},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43478959798812866},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.4300711452960968},{"id":"https://openalex.org/C2781118164","wikidata":"https://www.wikidata.org/wiki/Q1088113","display_name":"Chronic pain","level":2,"score":0.4112212359905243},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.2286277413368225},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C170493617","wikidata":"https://www.wikidata.org/wiki/Q208467","display_name":"Receptor","level":2,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2310.06075","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.06075","pdf_url":"https://arxiv.org/pdf/2310.06075","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2310.06075","doi":"https://doi.org/10.48550/arxiv.2310.06075","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2310.06075","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,"listed_in":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2310.06075","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.06075","pdf_url":"https://arxiv.org/pdf/2310.06075","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.9763000011444092,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387560685.pdf","grobid_xml":"https://content.openalex.org/works/W4387560685.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4229333777","https://openalex.org/W2788276467","https://openalex.org/W3133819740","https://openalex.org/W3212810877","https://openalex.org/W6999486993","https://openalex.org/W2978235527","https://openalex.org/W3186477727","https://openalex.org/W4388197856","https://openalex.org/W2943300066","https://openalex.org/W3164291498"],"abstract_inverted_index":{"Sickle":[0],"Cell":[1],"Disease":[2],"(SCD)":[3],"is":[4,35,63,88,101,109,152],"a":[5,126,176],"chronic":[6],"genetic":[7],"disorder":[8,34],"characterized":[9],"by":[10,104,160],"recurrent":[11],"acute":[12],"painful":[13,22,112],"episodes.":[14],"Opioids":[15],"are":[16],"often":[17,52],"used":[18],"to":[19,54,65,71,77,90,95,114,120,137,169],"manage":[20,74],"these":[21,48],"episodes;":[23],"the":[24,59,115,139],"extent":[25],"of":[26,38,42,47,81,132,199],"their":[27,75,79,85],"use":[28],"in":[29,32,58,125],"managing":[30],"pain":[31,56,69,93,122,140,193],"this":[33,133],"an":[36],"issue":[37],"debate.":[39],"The":[40],"risk":[41],"addiction":[43],"and":[44,111,164,212],"side":[45],"effects":[46],"opioid":[49],"treatments":[50],"can":[51,217],"lead":[53],"more":[55],"episodes":[57],"future.":[60],"Hence,":[61,173],"it":[62,100,108],"crucial":[64,153],"forecast":[66],"future":[67,192],"patient":[68,118,155,171],"trajectories":[70],"help":[72],"patients":[73,188],"SCD":[76],"improve":[78],"quality":[80],"life":[82],"without":[83],"compromising":[84],"treatment.":[86],"It":[87],"challenging":[89],"obtain":[91],"many":[92],"records":[94],"design":[96],"forecasting":[97,123,141],"models":[98,205],"since":[99],"mainly":[102],"recorded":[103],"patients'":[105,158],"self-report.":[106],"Therefore,":[107],"expensive":[110],"(due":[113],"need":[116],"for":[117,154,180,223],"compliance)":[119],"solve":[121,138],"problems":[124],"purely":[127],"supervised":[128],"manner.":[129],"In":[130],"light":[131],"challenge,":[134],"we":[135,174],"propose":[136,175],"problem":[142],"using":[143],"self-supervised":[144,177],"learning":[145,178],"methods.":[146],"Also,":[147],"clustering":[148,181],"such":[149],"time-series":[150,182],"data":[151],"phenotyping,":[156],"anticipating":[157],"prognoses":[159],"identifying":[161],"\"similar\"":[162],"patients,":[163],"designing":[165],"treatment":[166],"guidelines":[167],"tailored":[168],"homogeneous":[170],"subgroups.":[172],"approach":[179],"data,":[183],"where":[184],"each":[185],"cluster":[186],"comprises":[187],"who":[189],"share":[190],"similar":[191],"profiles.":[194],"Experiments":[195],"on":[196],"five":[197],"years":[198],"real-world":[200],"datasets":[201],"show":[202],"that":[203,216],"our":[204],"achieve":[206],"superior":[207],"performance":[208],"over":[209],"state-of-the-art":[210],"benchmarks":[211],"identify":[213],"meaningful":[214],"clusters":[215],"be":[218],"translated":[219],"into":[220],"actionable":[221],"information":[222],"clinical":[224],"decision-making.":[225]},"counts_by_year":[],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2023-10-12T00:00:00"}