{"id":"https://openalex.org/W7160053202","doi":"https://doi.org/10.48550/arxiv.2605.00738","title":"Temporal Data Requirement for Predicting Unplanned Hospital Readmissions","display_name":"Temporal Data Requirement for Predicting Unplanned Hospital Readmissions","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160053202","doi":"https://doi.org/10.48550/arxiv.2605.00738"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00738","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.00738","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006962414","display_name":"Ramin Mohammadi","orcid":"https://orcid.org/0000-0001-6033-7164"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammadi, Ramin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124772747","display_name":"Vahab Vahdat","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"vahdat, Vahab","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101511746","display_name":"Sarthak Jain","orcid":"https://orcid.org/0000-0002-4499-337X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jain, Sarthak","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061386632","display_name":"Amir T. Namin","orcid":"https://orcid.org/0000-0003-0669-7366"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Namin, Amir T.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118324134","display_name":"Ramya Palacholla","orcid":"https://orcid.org/0000-0002-1684-262X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Palacholla, Ramya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135177087","display_name":"Sagar Kamarthi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kamarthi, Sagar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.8604000210762024,"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":0.8604000210762024,"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/T10198","display_name":"Heart Failure Treatment and Management","score":0.029999999329447746,"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/T10562","display_name":"Total Knee Arthroplasty Outcomes","score":0.028999999165534973,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/suite","display_name":"Suite","score":0.590499997138977},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.4691999852657318},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45260000228881836},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.43299999833106995},{"id":"https://openalex.org/keywords/patient-record","display_name":"Patient record","score":0.4302000105381012},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4108000099658966},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4090999960899353},{"id":"https://openalex.org/keywords/medical-record","display_name":"Medical record","score":0.38960000872612}],"concepts":[{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.590499997138977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5735999941825867},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.4691999852657318},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45260000228881836},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43970000743865967},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.43299999833106995},{"id":"https://openalex.org/C2993731853","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Patient record","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4124000072479248},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4090999960899353},{"id":"https://openalex.org/C195910791","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Medical record","level":2,"score":0.38960000872612},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.384799987077713},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3723999857902527},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.3634999990463257},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.35989999771118164},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.34290000796318054},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C3018822202","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Patient data","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C545542383","wikidata":"https://www.wikidata.org/wiki/Q2751242","display_name":"Medical emergency","level":1,"score":0.25529998540878296},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00738","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.00738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00738","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,17,29,37,76,121,160,187],"proliferation":[2],"of":[3,31,39,83,114,177],"Electronic":[4],"Health":[5],"Records":[6],"(EHRs),":[7],"a":[8,81,112],"critical":[9],"challenge":[10,186],"in":[11],"building":[12],"predictive":[13,137],"models":[14,103],"is":[15,129],"determining":[16],"optimal":[18,122],"historical":[19,192],"data":[20,109,157,193],"time":[21,123,161],"window":[22,124,162],"to":[23,41,146,150],"maximize":[24],"accuracy.":[25],"This":[26],"study":[27],"investigates":[28],"impact":[30],"various":[32],"observation":[33],"windows":[34],"ranging":[35],"from":[36,69,75,143],"day":[38],"surgery":[40],"three":[42,145],"years":[43],"prior":[44,149],"on":[45],"predicting":[46],"30-day":[47],"readmission":[48,206],"following":[49],"hip":[50],"and":[51,64,92,111],"knee":[52],"arthroplasties.":[53],"The":[54],"dataset":[55],"encompasses":[56],"both":[57,115],"structured":[58,108,134,156],"encounter":[59],"records":[60],"(over":[61],"4":[62],"million)":[63],"unstructured":[65,126],"clinical":[66,77,105,127],"notes":[67,106,128,142],"(80,000)":[68],"7,174":[70],"patients.":[71],"To":[72],"extract":[73],"meaning":[74],"notes,":[78],"we":[79],"employed":[80],"suite":[82],"non":[84],"neural":[85,93],"(BOW,":[86],"count":[87],"BOW,":[88],"TF":[89],"IDF,":[90],"LDA)":[91],"encoders":[94],"(BERT,":[95],"1D":[96],"CNN,":[97],"BiLSTM,":[98],"Average).":[99],"We":[100],"subsequently":[101],"evaluated":[102],"utilizing":[104],"alone,":[107,110],"combination":[113],"modalities.":[116],"Our":[117],"results":[118],"demonstrate":[119],"that":[120,190],"for":[125,133,204],"significantly":[130],"shorter":[131],"than":[132],"data,":[135],"maximum":[136],"performance":[138,154],"was":[139],"achieved":[140],"using":[141,155],"just":[144],"six":[147],"months":[148],"surgery.":[151],"In":[152],"contrast,":[153],"improved":[158],"as":[159],"lengthened,":[163],"but":[164],"strictly":[165],"plateaued":[166],"after":[167],"twelve":[168],"months.":[169],"These":[170],"modality-specific":[171],"temporal":[172],"patterns":[173],"remained":[174],"consistent":[175],"regardless":[176],"model":[178],"complexity":[179],"or":[180],"encoder":[181],"type.":[182],"Ultimately,":[183],"these":[184],"findings":[185],"general":[188],"assumption":[189],"more":[191],"inherently":[194],"yields":[195],"better":[196],"machine":[197],"learning":[198],"predictions,":[199],"establishing":[200],"targeted":[201],"time-window":[202],"guidelines":[203],"optimizing":[205],"prediction":[207],"models.":[208]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-05T00:00:00"}
