{"id":"https://openalex.org/W4406461358","doi":"https://doi.org/10.1109/bigdata62323.2024.10826009","title":"LLM4cast: Repurposed LLM for Viral Disease Forecasting","display_name":"LLM4cast: Repurposed LLM for Viral Disease Forecasting","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406461358","doi":"https://doi.org/10.1109/bigdata62323.2024.10826009"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10826009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10826009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5079858913","display_name":"Farah Saeed","orcid":null},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Farah Saeed","raw_affiliation_strings":["University of Georgia,School of Computing,Athens,GA,USA"],"affiliations":[{"raw_affiliation_string":"University of Georgia,School of Computing,Athens,GA,USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003950043","display_name":"Mohammed S. Al\u2010Dosari","orcid":"https://orcid.org/0000-0001-8980-6606"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammed Aldosari","raw_affiliation_strings":["University of Georgia,School of Computing,Athens,GA,USA"],"affiliations":[{"raw_affiliation_string":"University of Georgia,School of Computing,Athens,GA,USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055430403","display_name":"I. Budak Arpinar","orcid":"https://orcid.org/0000-0001-5765-3385"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ismailcem Budak Arpinar","raw_affiliation_strings":["University of Georgia,School of Computing,Athens,GA,USA"],"affiliations":[{"raw_affiliation_string":"University of Georgia,School of Computing,Athens,GA,USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023847957","display_name":"John A. Miller","orcid":"https://orcid.org/0009-0003-8145-1849"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John A Miller","raw_affiliation_strings":["University of Georgia,School of Computing,Athens,GA,USA"],"affiliations":[{"raw_affiliation_string":"University of Georgia,School of Computing,Athens,GA,USA","institution_ids":["https://openalex.org/I165733156"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5079858913"],"corresponding_institution_ids":["https://openalex.org/I165733156"],"apc_list":null,"apc_paid":null,"fwci":0.3641,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.71046708,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1428","last_page":"1433"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9401000142097473,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9401000142097473,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9319000244140625,"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/computer-science","display_name":"Computer science","score":0.5058616995811462}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5058616995811462}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10826009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10826009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W2129535614","https://openalex.org/W3094502228","https://openalex.org/W3134307371","https://openalex.org/W3168867926","https://openalex.org/W3212890323","https://openalex.org/W4236362309","https://openalex.org/W4303438998","https://openalex.org/W4310416691","https://openalex.org/W4321854470","https://openalex.org/W4322718191","https://openalex.org/W4361229539","https://openalex.org/W4367694053","https://openalex.org/W4387355843","https://openalex.org/W4387796530","https://openalex.org/W4387969125","https://openalex.org/W4388297464","https://openalex.org/W4389115270","https://openalex.org/W4389664922","https://openalex.org/W4391591184","https://openalex.org/W4391871574","https://openalex.org/W4392781393","https://openalex.org/W4394007667","https://openalex.org/W4395065100","https://openalex.org/W4396758709","https://openalex.org/W6784333009","https://openalex.org/W6791141078","https://openalex.org/W6796581206","https://openalex.org/W6797155008","https://openalex.org/W6810637551","https://openalex.org/W6845625448","https://openalex.org/W6845816046","https://openalex.org/W6846825190","https://openalex.org/W6850493425","https://openalex.org/W6850625674","https://openalex.org/W6851948999","https://openalex.org/W6852465499","https://openalex.org/W6856804839","https://openalex.org/W6857005926","https://openalex.org/W6857477353","https://openalex.org/W6859368062","https://openalex.org/W6861489518","https://openalex.org/W6861928242","https://openalex.org/W6862399047","https://openalex.org/W6863884405"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Viral":[0],"diseases":[1],"have":[2,66],"had":[3],"a":[4,47,53,57,79,89,104,118,130,143],"significant":[5,213],"impact":[6],"on":[7,52,72],"millions":[8],"of":[9,21,26,32,207,218],"people":[10],"worldwide.":[11],"Utilizing":[12],"time":[13,90,176,193],"series":[14,91,177,194],"forecasting":[15],"methods":[16,40],"allows":[17],"for":[18,56,112,133,146,220],"the":[19,24,30,126,139,159,170,205,216],"estimation":[20,39],"cases,":[22],"facilitating":[23],"control":[25],"disease":[27,208],"spread":[28],"and":[29,99,109,137,155],"allocation":[31],"necessary":[33],"resources":[34],"in":[35,166,204,215],"medical":[36],"facilities.":[37],"Traditional":[38],"use":[41],"model":[42,48,92,108],"per":[43],"dataset":[44,55],"methodology":[45],"where":[46],"is":[49,152,164,202],"trained":[50,165,200,227],"only":[51],"single":[54],"disease.":[58],"However,":[59],"foundation":[60,221],"models":[61,65,222,226],"including":[62],"large":[63,106],"language":[64,107],"shown":[67],"improved":[68],"results":[69,211],"by":[70],"training":[71,189],"multiple":[73,94],"datasets":[74,95],"before":[75],"being":[76],"applied":[77],"to":[78,87,142,157,224],"target":[80],"dataset.":[81],"Following":[82],"this":[83],"strategy,":[84],"we":[85,173],"aim":[86],"train":[88,174],"using":[93,175,190],"from":[96,150,179,228],"diverse":[97,180],"domains":[98,181],"viral":[100,197],"diseases.":[101,198],"We":[102,116],"utilize":[103],"pretrained":[105,144],"adapt":[110],"it":[111],"estimating":[113],"Influenza-Like":[114],"Illness.":[115],"propose":[117],"novel":[119],"network":[120,201],"architecture":[121],"called":[122],"LLM4cast":[123],"that":[124],"encodes":[125],"input":[127],"patches":[128,141],"through":[129],"bidirectional":[131],"encoder":[132],"rich":[134],"embedding":[135],"extraction":[136],"passes":[138],"encoded":[140],"TinyLlama":[145,151],"fine-tuning.":[147],"The":[148,162,185,199,210],"output":[149],"then":[153],"flattened":[154],"projected":[156],"estimate":[158],"probable":[160],"cases.":[161,209],"framework":[163],"two":[167],"stages.":[168],"In":[169],"first":[171],"stage,":[172],"data":[178,195],"with":[182],"2.56M":[183],"timesteps.":[184],"second":[186],"stage":[187],"involves":[188],"domain":[191],"specific":[192],"about":[196],"used":[203],"evaluation":[206],"demonstrate":[212],"improvement":[214],"accuracy":[217],"forecasts":[219],"compared":[223],"state-of-the-art":[225],"scratch.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}