Artificial intelligence and machine learning for hemorrhagic trauma care.

"table 1 summary of main studies included in the review (see additional file 1 : table s2 for full study summaries) authors year purpose methodology features dataset used dataset size auroc (or relevant performance metric) ahmed et al[ ] 2020 mortality prediction model dnn age inr pt ptt haemoglobin hematocrit wbc platelets creatinine glucose lactate mimic iii 3041 auroc: 0 912 kilic et al [ ] 2010 determining time period for calculation and evaluation of trauma severity and predicted mortality after a period of resuscitation fuzzy-logic inference system sbp gcs changes after 1 h of resuscitation data from hospital/er records150 auroc: 0 925 kuo et al [ ] 2018 mortality prediction of motorcycle riders suffering traumatic injuries svm age sbp hr rr rbc platelet haemoglobin hematocrit gcs ais iss data from hospital/er records946 auroc: 0 9532 maurer et al [ ] and el hechi et al [ ] 2021 trauma-outcome predictor (top) smartphone tool top age sbp hr rr spo 2 temperature comorbidities gcs injury mechanism ais acs-tqip 934053 auroc (penetrating trauma: 0 920 blunt trauma: 0 830) cardosi et al [ ] 2021 predicting trauma patient mortality xgboost age spo 2 pr rr temperature gcs injury type ntdb 2007485 auroc (children data: 0 910 adult data: 0 890 all aged data: 0 900) lee et al [ ] 2021 prognostic prediction for critical decision-making xgboost age hr rr map gcs ais data from hospital/er records2232 auroc: 0 940 tran et al [ ] 2021 mortality prediction model xgboost injury mechanism ntdb 1611063 auroc: 0 863 tsiklidis et al [ ] 2020 outcome predictor for survival gradient boost age sbp hr rr temperature spo 2 gcs ntdb 799233 auroc: 0 924 becalick et al [ ] 2001 assessing probability of survival after trauma ann age rr sbp spo 2 hr injury type ais iss gcs uktarn 2042 auroc: 0 921 sefrioui et al [ ] 2017 predicting patient survival using readily available variables svm age injury type bp gcs rr ntdb 656092 auroc: 0 931 batchinsky et al [ ] 2009 predicting life-saving intervention based on ekg derived data ann heart rate complexity usaisr trauma 262 auroc: 0 868 liu et al [ ] 2017 predicting life-saving intervention mlp hr sbp dbp map rr spo 2 si pr wvsm trial 79 auroc: 0 990 liu et al [ ] 2018 predicting life-saving intervention mlp hr sbp dbp map rr spo 2 si pr wvsm trial 104 correlation coefficient: 0 779 kim et al [ ] 2018 2021 decision-making algorithm for remote triaging dnn age hr sbp si scs ntdb 1204290 auroc: 0 890 scerbo et al [ ] 2014 ml model for triaging trauma patients rf age hr sbp dbp spo 2 rr gcs injury type data from hospital/er records1653 sensitivity: 0 890 specificity: 0 420 nederpelt et al [ ] 2021 in-field triage tool for determining shock mt need for major surgery dirichlet dnn age bmi hr sbp rr temperature gcs injury location acs-tqip 29816 auroc (shock: 0 890 mt: 0 860 need for major surgery: 0 820) follin et al [ ] 2016 predicting need for specialized trauma care dt age hr spo 2 sbp gcs iss injury mechanism data from anonymized prospective trauma registry 1160 auroc: 0 820 mina et al [ ] and hodgman et al [ ] 2013 2018 smartphone app for predicting massive transfusion cases lasso regression mechanism of injury hr sbp bd iss rbc resuscitation intensity data from hospital/er records."

"Declarations Ethics approval and consent to participateNot applicable. Consent for publicationNot applicable. Competing interestsNone of the authors has any competing interests pertaining to this work. Competing interests None of the authors has any competing interests pertaining to this work."

"Funding Defence Research and Development Canada, Program Activity PEOPLE_014."