{"id":"https://openalex.org/W4411549657","doi":"https://doi.org/10.1145/3701716.3715545","title":"TRACE: Intra-visit Clinical Event Nowcasting via Effective Patient Trajectory Encoding","display_name":"TRACE: Intra-visit Clinical Event Nowcasting via Effective Patient Trajectory Encoding","publication_year":2025,"publication_date":"2025-05-08","ids":{"openalex":"https://openalex.org/W4411549657","doi":"https://doi.org/10.1145/3701716.3715545"},"language":"en","primary_location":{"id":"doi:10.1145/3701716.3715545","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3701716.3715545","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2503.23072","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037231384","display_name":"Y. T. Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuyang Liang","raw_affiliation_strings":["The Chinese University of Hong Kong, Shenzhen, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0009-2398-2477","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Shenzhen, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053712210","display_name":"Yankai Chen","orcid":"https://orcid.org/0000-0001-5741-2047"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yankai Chen","raw_affiliation_strings":["Cornell University, Ithaca, New York, USA"],"raw_orcid":"https://orcid.org/0000-0001-5741-2047","affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, New York, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043494334","display_name":"Yixiang Fang","orcid":"https://orcid.org/0000-0002-5047-8593"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixiang Fang","raw_affiliation_strings":["The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-5047-8593","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061340195","display_name":"Laks V. S. Lakshmanan","orcid":"https://orcid.org/0000-0002-9775-4241"},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Laks. V.S. Lakshmanan","raw_affiliation_strings":["University of British Columbia, Vancouver, British Columbia, Canada"],"raw_orcid":"https://orcid.org/0000-0002-9775-4241","affiliations":[{"raw_affiliation_string":"University of British Columbia, Vancouver, British Columbia, Canada","institution_ids":["https://openalex.org/I141945490"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055857919","display_name":"Chenhao Ma","orcid":"https://orcid.org/0000-0002-3243-8512"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenhao Ma","raw_affiliation_strings":["The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-3243-8512","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I4210116924"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5037231384"],"corresponding_institution_ids":["https://openalex.org/I4210116924"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0673534,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1118","last_page":"1122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9995999932289124,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9995999932289124,"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/T10028","display_name":"Topic Modeling","score":0.9952999949455261,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/nowcasting","display_name":"Nowcasting","score":0.9304071664810181},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.7784088850021362},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7599366903305054},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6729404926300049},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6384043097496033},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.591456413269043},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3196185231208801},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.1471787989139557},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10764861106872559}],"concepts":[{"id":"https://openalex.org/C2781013037","wikidata":"https://www.wikidata.org/wiki/Q1433331","display_name":"Nowcasting","level":2,"score":0.9304071664810181},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.7784088850021362},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7599366903305054},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6729404926300049},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6384043097496033},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.591456413269043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3196185231208801},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.1471787989139557},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10764861106872559},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3701716.3715545","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3701716.3715545","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2503.23072","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2503.23072","pdf_url":"https://arxiv.org/pdf/2503.23072","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2503.23072","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2503.23072","pdf_url":"https://arxiv.org/pdf/2503.23072","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2690721124","https://openalex.org/W2896538705","https://openalex.org/W2949227999","https://openalex.org/W2997653844","https://openalex.org/W3100272281","https://openalex.org/W4283366068","https://openalex.org/W4385212602","https://openalex.org/W4393147760","https://openalex.org/W4393159476","https://openalex.org/W4403582564","https://openalex.org/W4404783722","https://openalex.org/W6600175266","https://openalex.org/W6600688380","https://openalex.org/W6601289607","https://openalex.org/W6602127427"],"related_works":["https://openalex.org/W2098567841","https://openalex.org/W3124209601","https://openalex.org/W2238969010","https://openalex.org/W2189249776","https://openalex.org/W1526105959","https://openalex.org/W3193710367","https://openalex.org/W3122492506","https://openalex.org/W4388046081","https://openalex.org/W1778290058","https://openalex.org/W3124577894"],"abstract_inverted_index":{"Electronic":[0],"Health":[1],"Records":[2],"(EHR)":[3],"have":[4,21],"become":[5],"a":[6,10,57,75,97,113],"valuable":[7],"resource":[8],"for":[9,79,116,144],"wide":[11],"range":[12],"of":[13,31,52,108,121],"predictive":[14],"tasks":[15],"in":[16,69],"healthcare.":[17],"However,":[18],"existing":[19],"approaches":[20],"largely":[22],"focused":[23],"on":[24,125],"inter-visit":[25],"event":[26,81],"predictions,":[27],"overlooking":[28],"the":[29,50,62,119,122,134],"importance":[30],"intra-visit":[32],"nowcasting,":[33],"which":[34],"provides":[35],"prompt":[36],"clinical":[37,80],"insights":[38],"during":[39],"an":[40],"ongoing":[41],"patient":[42,85,146],"visit.":[43,59],"To":[44],"address":[45],"this":[46],"gap,":[47],"we":[48,111],"introduce":[49,112],"task":[51],"laboratory":[53,63,151],"measurement":[54,152],"prediction":[55],"within":[56],"hospital":[58],"We":[60,72],"study":[61],"data":[64],"that,":[65],"however,":[66],"remained":[67],"underexplored":[68],"previous":[70,139],"work.":[71],"propose":[73],"TRACE,":[74],"Transformer-based":[76],"model":[77,136],"designed":[78],"nowcasting":[82],"by":[83],"encoding":[84],"trajectories.":[86],"TRACE":[87],"effectively":[88],"handles":[89],"long":[90],"sequences":[91],"and":[92,105],"captures":[93],"temporal":[94],"dependencies":[95],"through":[96,148],"novel":[98],"timestamp":[99],"embedding":[100],"that":[101,133],"integrates":[102],"decay":[103],"properties":[104],"periodic":[106],"patterns":[107],"data.":[109],"Additionally,":[110],"smoothed":[114],"mask":[115],"denoising,":[117],"improving":[118,145],"robustness":[120],"model.":[123],"Experiments":[124],"two":[126],"large-scale":[127],"electronic":[128],"health":[129],"record":[130],"datasets":[131],"demonstrate":[132],"proposed":[135],"significantly":[137],"outperforms":[138],"methods,":[140],"highlighting":[141],"its":[142],"potential":[143],"care":[147],"more":[149],"accurate":[150],"nowcasting.":[153],"The":[154],"code":[155],"is":[156],"available":[157],"at":[158],"https://github.com/Amehi/TRACE.":[159]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}