{"id":"https://openalex.org/W4412183804","doi":"https://doi.org/10.1007/s11517-025-03414-x","title":"Rapid trauma classification under data scarcity: an emergency on-scene decision model combining natural language processing and machine learning","display_name":"Rapid trauma classification under data scarcity: an emergency on-scene decision model combining natural language processing and machine learning","publication_year":2025,"publication_date":"2025-07-11","ids":{"openalex":"https://openalex.org/W4412183804","doi":"https://doi.org/10.1007/s11517-025-03414-x","pmid":"https://pubmed.ncbi.nlm.nih.gov/40643792"},"language":"en","primary_location":{"id":"doi:10.1007/s11517-025-03414-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11517-025-03414-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11517-025-03414-x.pdf","source":{"id":"https://openalex.org/S46300952","display_name":"Medical & Biological Engineering & Computing","issn_l":"0140-0118","issn":["0140-0118","1741-0444"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical & Biological Engineering & Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11517-025-03414-x.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103392241","display_name":"Jun Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I151075929","display_name":"Army Medical University","ror":"https://ror.org/05w21nn13","country_code":"CN","type":"education","lineage":["https://openalex.org/I151075929"]},{"id":"https://openalex.org/I4210092099","display_name":"Daping Hospital","ror":"https://ror.org/00fthae95","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210092099"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Tang","raw_affiliation_strings":["Department of Information, Daping Hospital, Army Medical University, Chongqing, 400042, China"],"affiliations":[{"raw_affiliation_string":"Department of Information, Daping Hospital, Army Medical University, Chongqing, 400042, China","institution_ids":["https://openalex.org/I4210092099","https://openalex.org/I151075929"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100611332","display_name":"Tao Li","orcid":"https://orcid.org/0000-0002-9362-9718"},"institutions":[{"id":"https://openalex.org/I151075929","display_name":"Army Medical University","ror":"https://ror.org/05w21nn13","country_code":"CN","type":"education","lineage":["https://openalex.org/I151075929"]},{"id":"https://openalex.org/I4210092099","display_name":"Daping Hospital","ror":"https://ror.org/00fthae95","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210092099"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Li","raw_affiliation_strings":["Department of Shock and Transfusion, State Key Laboratory of Trauma, Burns and Combined Injury, Daping Hospital, Army Medical University, Chongqing, 400042, China"],"affiliations":[{"raw_affiliation_string":"Department of Shock and Transfusion, State Key Laboratory of Trauma, Burns and Combined Injury, Daping Hospital, Army Medical University, Chongqing, 400042, China","institution_ids":["https://openalex.org/I151075929","https://openalex.org/I4210092099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101514260","display_name":"Liangming Liu","orcid":"https://orcid.org/0000-0002-0352-8949"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liangming Liu","raw_affiliation_strings":["Department of Shock and Transfusion, State Key Laboratory of Trauma, Burns and Combined Injury, Daping Hospital, Army Medical University, Chongqing, 400042, China. lmliu62@tmmu.edu.cn"],"affiliations":[{"raw_affiliation_string":"Department of Shock and Transfusion, State Key Laboratory of Trauma, Burns and Combined Injury, Daping Hospital, Army Medical University, Chongqing, 400042, China. lmliu62@tmmu.edu.cn","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037798970","display_name":"Dongdong Wu","orcid":"https://orcid.org/0000-0001-7826-5963"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongdong Wu","raw_affiliation_strings":["Department of Information, Daping Hospital, Army Medical University, Chongqing, 400042, China. 604269346@qq.com"],"affiliations":[{"raw_affiliation_string":"Department of Information, Daping Hospital, Army Medical University, Chongqing, 400042, China. 604269346@qq.com","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103392241"],"corresponding_institution_ids":["https://openalex.org/I151075929","https://openalex.org/I4210092099"],"apc_list":{"value":2890,"currency":"EUR","value_usd":3690},"apc_paid":{"value":2890,"currency":"EUR","value_usd":3690},"fwci":2.5245,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.88802401,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"63","issue":"12","first_page":"3521","last_page":"3530"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11467","display_name":"Trauma and Emergency Care Studies","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11467","display_name":"Trauma and Emergency Care Studies","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency 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/T11095","display_name":"Emergency and Acute Care Studies","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency 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/T10645","display_name":"Cardiac Arrest and Resuscitation","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"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/computer-science","display_name":"Computer science","score":0.7299109697341919},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7126466035842896},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6613141298294067},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.6106069684028625},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4192828834056854},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.41383615136146545},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37543874979019165},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2512813210487366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7299109697341919},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7126466035842896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6613141298294067},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.6106069684028625},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4192828834056854},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.41383615136146545},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37543874979019165},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2512813210487366}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004632","descriptor_name":"Emergency Medical Services","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004632","descriptor_name":"Emergency Medical Services","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004632","descriptor_name":"Emergency Medical Services","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004632","descriptor_name":"Emergency Medical Services","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014947","descriptor_name":"Wounds and Injuries","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":true},{"descriptor_ui":"D014947","descriptor_name":"Wounds and Injuries","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":true},{"descriptor_ui":"D014947","descriptor_name":"Wounds and Injuries","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":true},{"descriptor_ui":"D014947","descriptor_name":"Wounds and Injuries","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1007/s11517-025-03414-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11517-025-03414-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11517-025-03414-x.pdf","source":{"id":"https://openalex.org/S46300952","display_name":"Medical & Biological Engineering & Computing","issn_l":"0140-0118","issn":["0140-0118","1741-0444"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical & Biological Engineering & Computing","raw_type":"journal-article"},{"id":"pmid:40643792","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40643792","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical & biological engineering & computing","raw_type":null}],"best_oa_location":{"id":"doi:10.1007/s11517-025-03414-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11517-025-03414-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11517-025-03414-x.pdf","source":{"id":"https://openalex.org/S46300952","display_name":"Medical & Biological Engineering & Computing","issn_l":"0140-0118","issn":["0140-0118","1741-0444"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical & Biological Engineering & Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5199999809265137,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412183804.pdf","grobid_xml":"https://content.openalex.org/works/W4412183804.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2009232865","https://openalex.org/W2056208640","https://openalex.org/W3046918297","https://openalex.org/W4255541163","https://openalex.org/W4293108385","https://openalex.org/W4295910800","https://openalex.org/W4312621205","https://openalex.org/W4384565153","https://openalex.org/W4388485126","https://openalex.org/W4388870005","https://openalex.org/W4389663541","https://openalex.org/W4391855109","https://openalex.org/W4394767219","https://openalex.org/W4398247354","https://openalex.org/W4404461428","https://openalex.org/W4410849485","https://openalex.org/W6888573545"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W2147697413","https://openalex.org/W2154063878","https://openalex.org/W4231274751","https://openalex.org/W3082059448","https://openalex.org/W4313640622","https://openalex.org/W4239608116","https://openalex.org/W2002382339"],"abstract_inverted_index":{"Trauma":[0],"has":[1],"become":[2],"a":[3,66,107,114],"major":[4],"cause":[5],"of":[6,17,29,41,71,86,144,211,219,228,249,291],"increased":[7],"morbidity":[8],"and":[9,36,59,74,103,128,179,226,234,247,253,263,270],"mortality":[10],"worldwide.":[11],"In":[12],"emergency":[13,44,75,118,284],"response,":[14],"the":[15,27,30,38,63,80,84,168,183,187,199,205,220,238,241,259,283],"classification":[16],"injuries":[18],"is":[19,77,214],"crucial":[20],"as":[21],"it":[22,51],"helps":[23],"to":[24,56,97,140,153],"quickly":[25],"determine":[26],"criticality":[28],"injured,":[31],"allocate":[32],"rescue":[33,54,76,81,87],"resources":[34],"rationally,":[35],"decide":[37],"priority":[39],"order":[40],"treatment.":[42],"However,":[43],"scenes":[45],"are":[46],"often":[47],"chaotic":[48],"environments,":[49],"making":[50],"difficult":[52],"for":[53],"personnel":[55],"collect":[57],"complete":[58],"accurate":[60],"information":[61],"about":[62],"injured":[64],"in":[65,186,289],"short":[67],"period.":[68],"The":[69,133,172,223],"combination":[70],"artificial":[72],"intelligence":[73],"gradually":[78],"changing":[79],"model,":[82],"improving":[83],"efficiency":[85],"operations.":[88],"We":[89,105],"selected":[90],"data":[91,120,165,281],"from":[92,167,282],"26,810":[93],"trauma":[94],"patients":[95],"admitted":[96],"Chongqing":[98,169],"Daping":[99],"Hospital":[100],"between":[101],"2013":[102],"2024.":[104],"propose":[106],"fast":[108],"tiered":[109,134,275],"medical":[110,135,276],"treatment":[111,136,277],"method":[112],"with":[113,286],"two-layer":[115,188],"structure":[116],"under":[117],"limited":[119,280],"conditions,":[121],"which":[122,213],"integrates":[123],"natural":[124],"language":[125],"processing":[126],"(NLP)":[127],"machine":[129],"learning":[130],"(ML)":[131],"techniques.":[132],"model":[137,197,203,230,242,267],"utilizes":[138],"NLP":[139,269],"capture":[141],"semantic":[142],"features":[143],"unstructured":[145],"text":[146],"data,":[147],"while":[148],"utilizing":[149],"four":[150],"ML":[151,189,271],"algorithms":[152],"process":[154],"structured":[155],"numerical":[156],"data.":[157],"Additionally,":[158],"we":[159],"conducted":[160],"external":[161,239],"validation":[162],"using":[163],"245":[164],"entries":[166],"Emergency":[170],"Center.":[171],"experimental":[173],"results":[174,257],"show":[175],"that":[176,218],"gradient":[177],"boosting":[178],"logistic":[180],"regression":[181],"have":[182],"best":[184],"performance":[185],"algorithms.":[190],"Based":[191],"on":[192,204,279],"these":[193],"two":[194],"algorithms,":[195],"our":[196,229],"outperformed":[198],"multilayer":[200],"perceptron":[201],"(MLP)":[202],"test":[206],"dataset,":[207,240],"achieving":[208],"an":[209],"accuracy":[210],"91.17%,":[212],"4.33%":[215],"higher":[216],"than":[217],"MLP":[221],"model.":[222],"specificity,":[224,245],"F1-score,":[225,246],"AUC":[227,248],"were":[231],"97.06%,":[232],"86.85%,":[233],"0.949,":[235],"respectively.":[236,255],"For":[237],"achieved":[243],"accuracy,":[244],"87.35%,":[250],"95.78%,":[251],"80.37%,":[252],"0.848,":[254],"These":[256],"demonstrate":[258],"model's":[260],"high":[261],"generalizability":[262],"prediction":[264,292],"accuracy.":[265,293],"A":[266],"integrating":[268],"techniques":[272],"enables":[273],"rapid":[274],"based":[278],"scene,":[285],"significant":[287],"advantages":[288],"terms":[290]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}