{"id":"https://openalex.org/W7152654738","doi":"https://doi.org/10.48550/arxiv.2604.06214","title":"Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions","display_name":"Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7152654738","doi":"https://doi.org/10.48550/arxiv.2604.06214"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.06214","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06214","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.2604.06214","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120713599","display_name":"Sadaf Tabatabaee","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tabatabaee, Sadaf","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5053471743","display_name":"Sarah S. Lam","orcid":"https://orcid.org/0000-0002-7726-6179"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lam, Sarah S.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5120713599"],"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":0.6137999892234802,"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":0.6137999892234802,"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/T10916","display_name":"Surgical Simulation and Training","score":0.08229999989271164,"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"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.07580000162124634,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/categorization","display_name":"Categorization","score":0.5266000032424927},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5182999968528748},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5160999894142151},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4738999903202057},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47290000319480896},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.45559999346733093},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.43689998984336853},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3995000123977661}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7565000057220459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6424999833106995},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5266000032424927},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5182999968528748},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5160999894142151},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4738999903202057},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4652000069618225},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.43689998984336853},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.391400009393692},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C111168008","wikidata":"https://www.wikidata.org/wiki/Q1136838","display_name":"Self-organizing map","level":3,"score":0.32260000705718994},{"id":"https://openalex.org/C69505689","wikidata":"https://www.wikidata.org/wiki/Q455338","display_name":"Unified Medical Language System","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C154874363","wikidata":"https://www.wikidata.org/wiki/Q3518464","display_name":"Medical classification","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2840999960899353},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2799000144004822},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2727999985218048},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.06214","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06214","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.2604.06214","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06214","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Efficient":[0],"classification":[1,127],"of":[2,81,165],"surgical":[3,30,46,179],"procedures":[4],"by":[5],"urgency":[6,34],"is":[7,124,131],"paramount":[8],"to":[9,27],"optimize":[10],"patient":[11,187],"care":[12],"and":[13,38,66,83,90,106,136,143,149,174,186],"resource":[14],"allocation":[15],"within":[16],"healthcare":[17],"systems.":[18],"This":[19,157],"study":[20],"introduces":[21],"an":[22],"unsupervised":[23,158],"neural":[24,111],"network":[25,112],"approach":[26],"automatically":[28],"categorize":[29],"transcriptions":[31],"into":[32,50],"three":[33],"levels:":[35],"immediate,":[36],"urgent,":[37],"elective.":[39],"Leveraging":[40],"BioClinicalBERT,":[41],"a":[42,110,172],"domain-specific":[43],"language":[44],"model,":[45],"transcripts":[47],"are":[48,60],"transformed":[49],"high-dimensional":[51],"embeddings":[52,59,123],"that":[53,113],"capture":[54],"their":[55],"semantic":[56],"nuances.":[57],"These":[58],"subsequently":[61],"clustered":[62],"using":[63,134],"both":[64],"K-means":[65],"Deep":[67],"Embedding":[68],"Clustering":[69],"(DEC)":[70],"algorithms,":[71],"in":[72,78,189],"which":[73,102,145,181],"DEC":[74],"demonstrates":[75],"superior":[76],"performance":[77,148],"the":[79,92,98,163],"formation":[80],"cohesive":[82],"well-separated":[84],"clusters.":[85],"To":[86],"ensure":[87],"clinical":[88],"relevance":[89],"accuracy,":[91,140],"clustering":[93],"results":[94],"undergo":[95],"validation":[96],"through":[97],"Modified":[99],"Delphi":[100],"Method,":[101],"involves":[103],"expert":[104],"review":[105],"refinement.":[107],"Following":[108],"validation,":[109],"integrates":[114],"Bidirectional":[115],"Long":[116],"Short-Term":[117],"Memory":[118],"(BiLSTM)":[119],"layers":[120],"with":[121],"BioClinicalBERT":[122],"developed":[125],"for":[126,177],"tasks.":[128],"The":[129],"model":[130],"rigorously":[132],"evaluated":[133],"cross-validation":[135],"metrics":[137],"such":[138],"as":[139],"precision,":[141],"recall,":[142],"F1-score,":[144],"achieve":[146],"robust":[147],"demonstrate":[150],"strong":[151],"generalization":[152],"capabilities":[153],"on":[154],"unseen":[155],"data.":[156],"framework":[159],"not":[160],"only":[161],"addresses":[162],"challenge":[164],"limited":[166],"labeled":[167],"data":[168],"but":[169],"also":[170],"provides":[171],"scalable":[173],"reliable":[175],"solution":[176],"real-time":[178],"prioritization,":[180],"ultimately":[182],"enhances":[183],"operational":[184],"efficiency":[185],"outcomes":[188],"dynamic":[190],"medical":[191],"environments.":[192]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-04-10T00:00:00"}
