Bigger Is Not Always Better: Computational Efficiency in Lexical Prehospital Triage Modeling
{"INTRODUCTION:":[0],"In":[1],"future":[2],"large-scale":[3],"combat":[4,338],"operations,":[5],"military":[6,324],"medics":[7],"will":[8],"likely":[9],"face":[10,17],"staggering":[11],"numbers":[12],"of":[13,18,63,82,109,120,178,314,348],"casualties":[14],"in":[15,59,197,297,305,360],"the":[16,48,80,107,133,292,315,361],"resource":[19],"constrained":[20],"evacuation":[21],"and":[22,34,118,168,191,264,283,333,355],"forward":[23,362],"medical":[24,298,358],"care.":[25],"Artificial":[26],"intelligence":[27],"(AI)":[28],"may":[29,56],"enable":[30],"triage":[31,309],"at":[32],"scale":[33],"improve":[35],"operational":[36],"efficiencies.":[37],"Although":[38],"many":[39],"assume":[40],"that":[41],"sophisticated":[42],"large":[43],"language":[44],"models":[45,54,166],"(LLMs)":[46],"hold":[47],"most":[49],"promise,":[50],"simpler":[51,78],"machine":[52],"learning":[53],"(MLMs)":[55],"offer":[57,320],"advantages":[58,322],"edge":[60,330],"implementation":[61],"because":[62],"lower":[64],"computational":[65,71,249,316,349],"burden.":[66],"Here":[67],"we":[68],"search":[69],"for":[70,256,262,269,277,281,286,323,337],"efficiency-achieving":[72],"equivalent":[73,195],"clinical":[74],"performance":[75,196],"with":[76,158,171],"computationally":[77],"AI-in":[79],"domain":[81],"on-scene":[83,308],"triage.":[84],"MATERIALS":[85],"AND":[86],"METHODS:":[87],"We":[88],"conducted":[89],"a":[90,94,312],"retrospective":[91],"study":[92],"via":[93],"civilian":[95],"critical":[96,352],"care":[97],"air":[98],"transport":[99],"service":[100],"from":[101,132,146],"2012":[102],"to":[103,142,303,353],"2021":[104],"(approved":[105],"by":[106,115],"University":[108],"Pittsburgh":[110],"IRB).":[111],"Free-text":[112],"impressions":[113],"recorded":[114],"treating":[116],"clinicians":[117],"records":[119],"patient":[121],"lifesaving":[122],"intervention":[123],"(LSI;":[124],"e.g.,":[125],"airway":[126],"management;":[127],"blood":[128],"transfusion)":[129],"were":[130,140,181],"abstracted":[131],"electronic":[134],"health":[135],"record.":[136],"Two":[137],"modeling":[138],"approaches":[139],"used":[141],"predict":[143],"LSI":[144,199,242],"receipt":[145],"medic":[147],"impression":[148],"text.":[149],"MLMs":[150,190,300,319],"involved":[151,162],"using":[152,163],"an":[153],"ensemble":[154],"classifier":[155],"model":[156,272],"fed":[157,170],"word":[159,173],"frequencies.":[160],"LLMs":[161,192,245,304],"2":[164],"transformer":[165],"(DistilBERT":[167],"BioBERT)":[169],"tokenized":[172],"embeddings.":[174],"RESULTS:":[175],"A":[176],"total":[177],"12,913":[179],"patients":[180],"included":[182],"(mean":[183],"age":[184],"=":[185,202,207,214,219,226,231],"52.3":[186],"years,":[187],"63%":[188],"men).":[189],"produced":[193],"broadly":[194],"predicting":[198],"(MLM:":[200],"AUROC":[201,213,225],".793":[203],"[.776,":[204],".810];":[205],"AP":[206,218,230],".670":[208],"[.643,":[209],".695]":[210],"vs.":[211,223],"BioBERT:":[212],".803":[215,227],"[.796,":[216],".816];":[217],".674":[220],"[.649,":[221],".698])":[222],"DistilBERT:":[224],"[.785,":[228],".811];":[229],".673":[232],"[.651,":[233],".688]).":[234],"Broad":[235],"predictive":[236],"equipoise":[237],"held":[238],"across":[239,344],"7":[240,265],"independent":[241],"categories.":[243],"Yet":[244],"required":[246],"considerably":[247],"greater":[248],"resources:":[250],"Model":[251],"training":[252],"took":[253,274],"9.42":[254],"seconds":[255,261,268,276,280,285],"MLMs,":[257,278],"3":[258],"minutes":[259,266],"57":[260],"DistilBERT,":[263,282],"14":[267],"BioBERT,":[270],"whereas":[271],"prediction":[273],".048":[275],"7.37":[279],"13.36":[284],"BioBERT.":[287],"CONCLUSIONS:":[288],"Our":[289],"findings":[290],"counter":[291],"\\"bigger":[293],"is":[294],"better\\"":[295],"paradigm":[296],"AI:":[299],"performed":[301],"equivalently":[302],"task":[306],"mirroring":[307],"while":[310],"requiring":[311],"fraction":[313],"resources.":[317],"Nimble":[318],"several":[321],"AI,":[325],"including":[326],"seamless":[327],"deployment":[328],"on":[329],"computing":[331],"devices":[332],"explainable":[334],"decision":[335],"support":[336],"medics.":[339],"As":[340],"AI":[341],"technologies":[342],"expand":[343],"warfighting":[345],"domains,":[346],"considerations":[347],"efficiency":[350],"are":[351],"adopt":[354],"field":[356],"new":[357],"capabilities":[359],"environment.":[363]}
Authors
- Leonard Weiss (ORCID: https://orcid.org/0000-0001-6857-6573)
- Ronald K. Poropatich
- Aaron C. Weidman (ORCID: https://orcid.org/0000-0002-9395-4051)
- David D. Salcido (ORCID: https://orcid.org/0000-0002-3735-8167)
- Francis X Guyette
- Chase Zikmund
Institutions
- University of Pittsburgh (US)
Publication Details
- Journal
- Military Medicine
- Published
- 2026-07-26
- DOI
- https://doi.org/10.1093/milmed/usag325
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 4.24
Funders
- National Institutes of Health