{"id":"https://openalex.org/W7147480839","doi":"https://doi.org/10.48550/arxiv.2603.28167","title":"Automating Early Disease Prediction Via Structured and Unstructured Clinical Data","display_name":"Automating Early Disease Prediction Via Structured and Unstructured Clinical Data","publication_year":2026,"publication_date":"2026-03-30","ids":{"openalex":"https://openalex.org/W7147480839","doi":"https://doi.org/10.48550/arxiv.2603.28167"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.28167","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28167","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.28167","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114660719","display_name":"Ane Garc\u00eda Domingo-Aldama","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Domingo-Aldama, Ane G","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122121784","display_name":"Marcos Merino Prado","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prado, Marcos Merino","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062986535","display_name":"Alain Garc\u00eda-Olea","orcid":"https://orcid.org/0000-0002-8742-0600"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Olea, Alain Garc\u00eda","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132693796","display_name":"Josu Goikoetxea","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goikoetxea, Josu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030328561","display_name":"Koldo Gojenola","orcid":"https://orcid.org/0000-0002-2116-6611"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gojenola, Koldo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5005074637","display_name":"Aitziber Atutxa","orcid":"https://orcid.org/0000-0003-4512-8633"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atutxa, Aitziber","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5114660719"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.8907999992370605,"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":0.8907999992370605,"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/T10065","display_name":"Atrial Fibrillation Management and Outcomes","score":0.014299999922513962,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular 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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.013100000098347664,"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/unstructured-data","display_name":"Unstructured data","score":0.7293999791145325},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6699000000953674},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5544000267982483},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5235000252723694},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.40560001134872437},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.37310001254081726},{"id":"https://openalex.org/keywords/snomed-ct","display_name":"SNOMED CT","score":0.3257000148296356}],"concepts":[{"id":"https://openalex.org/C2781252014","wikidata":"https://www.wikidata.org/wiki/Q1141900","display_name":"Unstructured data","level":3,"score":0.7293999791145325},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6759999990463257},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6699000000953674},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5544000267982483},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5235000252723694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5123999714851379},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5019999742507935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4878000020980835},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.40560001134872437},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C206497026","wikidata":"https://www.wikidata.org/wiki/Q1753883","display_name":"SNOMED CT","level":3,"score":0.3257000148296356},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.28139999508857727},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C145642194","wikidata":"https://www.wikidata.org/wiki/Q870895","display_name":"Health informatics","level":3,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.28167","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28167","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.28167","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28167","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6905058026313782,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,73],"study":[1],"presents":[2],"a":[3],"fully":[4],"automated":[5],"methodology":[6,101],"for":[7,172],"early":[8,34,159],"prediction":[9,160],"studies":[10],"in":[11,84,102],"clinical":[12,64,144,155,173],"settings,":[13],"leveraging":[14],"information":[15,93,122],"extracted":[16],"from":[17],"unstructured":[18,154],"discharge":[19,25,45,120],"reports.":[20],"The":[21],"proposed":[22],"pipeline":[23],"uses":[24],"reports":[26,46],"to":[27,132],"support":[28],"the":[29,76,100,103,151,167],"three":[30],"main":[31],"steps":[32],"of":[33,79,105,153,169],"prediction:":[35],"cohort":[36],"selection,":[37],"dataset":[38],"generation,":[39],"and":[40,66,126,165],"outcome":[41],"labeling.":[42],"By":[43],"processing":[44,50],"with":[47,62,119,128],"natural":[48],"language":[49],"techniques,":[51],"we":[52],"can":[53,157],"efficiently":[54],"identify":[55],"relevant":[56,92],"patient":[57],"cohorts,":[58],"enrich":[59],"structured":[60,137],"datasets":[61,117],"additional":[63],"variables,":[65],"generate":[67],"high-quality":[68],"labels":[69],"without":[70],"manual":[71],"intervention.":[72],"approach":[74],"addresses":[75],"frequent":[77],"issue":[78],"missing":[80],"or":[81],"incomplete":[82],"data":[83,163],"codified":[85],"electronic":[86],"health":[87],"records":[88],"(EHR),":[89],"capturing":[90],"clinically":[91],"that":[94,112,149],"is":[95],"often":[96],"underrepresented.":[97],"We":[98],"evaluate":[99],"context":[104],"predicting":[106],"atrial":[107],"fibrillation":[108],"(AF)":[109],"progression,":[110],"showing":[111],"predictive":[113,170],"models":[114,133,171],"trained":[115,134],"on":[116,136],"enriched":[118],"report":[121],"achieve":[123],"higher":[124],"accuracy":[125],"correlation":[127],"true":[129],"outcomes":[130],"compared":[131],"solely":[135],"EHR":[138],"data,":[139],"while":[140],"also":[141],"surpassing":[142],"traditional":[143],"scores.":[145],"These":[146],"results":[147],"demonstrate":[148],"automating":[150],"integration":[152],"text":[156],"streamline":[158],"studies,":[161],"improve":[162],"quality,":[164],"enhance":[166],"reliability":[168],"decision-making.":[174]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-04-02T00:00:00"}