Table 1 Summary of main studies included in the review (see Additional file 1: Table S2 for full study summaries)
From: Artificial intelligence and machine learning for hemorrhagic trauma care
Authors | Year | Purpose | Methodology | Features | Dataset used | Dataset size | AUROC (or relevant performance metric) |
|---|---|---|---|---|---|---|---|
Ahmed et al.[27] | 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. [28] | 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 1Â h of resuscitation | Data from hospital/ER records | 150 | AUROC: 0.925 |
Kuo et al. [29] | 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 records | 946 | AUROC: 0.9532 |
2021 | Trauma-outcome predictor (TOP) smartphone tool | TOP | Age, SBP, HR, RR, SpO2, Temperature, comorbidities, GCS, injury mechanism, AIS | ACS-TQIP | 934,053 | AUROC (penetrating trauma: 0.920, blunt trauma: 0.830) | |
Cardosi et al. [32] | 2021 | Predicting trauma patient mortality | XGBoost | Age, SpO2, PR, RR, Temperature, GCS, injury type | NTDB | 2,007,485 | AUROC (children data: 0.910, adult data: 0.890, all aged data: 0.900) |
Lee et al. [33] | 2021 | Prognostic prediction for critical decision-making | XGBoost | Age, HR, RR, MAP, GCS, AIS | Data from hospital/ER records | 2232 | AUROC: 0.940 |
Tran et al. [34] | 2021 | Mortality prediction model | XGBoost | Injury mechanism | NTDB | 1,611,063 | AUROC: 0.863 |
Tsiklidis et al. [35] | 2020 | Outcome predictor for survival | Gradient Boost | Age, SBP, HR, RR, Temperature, SpO2, GCS | NTDB | 799,233 | AUROC: 0.924 |
Becalick et al. [36] | 2001 | Assessing probability of survival after trauma | ANN | Age, RR, SBP, SpO2, HR, Injury type, AIS, ISS, GCS | UKTARN | 2042 | AUROC: 0.921 |
Sefrioui et al. [37] | 2017 | Predicting patient survival using readily available variables | SVM | Age, injury type, BP, GCS, RR, | NTDB | 656,092 | AUROC: 0.931 |
Batchinsky et al. [38] | 2009 | Predicting life-saving intervention based on EKG derived data | ANN | Heart rate complexity | USAISR Trauma | 262 | AUROC: 0.868 |
Liu et al. [39] | 2017 | Predicting life-saving intervention | MLP | HR, SBP, DBP, MAP, RR, SpO2, SI, PR | WVSM trial | 79 | AUROC: 0.990 |
Liu et al. [40] | 2018 | Predicting life-saving intervention | MLP | HR, SBP, DBP, MAP, RR, SpO2, SI, PR | WVSM trial | 104 | Correlation coefficient: 0.779 |
2018, 2021 | Decision-making algorithm for remote triaging | DNN | Age, HR, SBP, SI, SCS | NTDB | 1,204,290 | AUROC: 0.890 | |
Scerbo et al. [43] | 2014 | ML model for triaging trauma patients | RF | Age, HR, SBP, DBP, SpO2, RR, GCS, injury type | Data from hospital/ER records | 1653 | Sensitivity: 0.890, Specificity: 0.420 |
Nederpelt et al. [44] | 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 | 29,816 | AUROC (shock: 0.890, MT: 0.860, need for major surgery: 0.820) |
Follin et al. [45] | 2016 | Predicting need for specialized trauma care | DT | Age, HR, SpO2, SBP, GCS, ISS, injury mechanism | Data from anonymized prospective trauma registry | 1160 | AUROC: 0.820 |
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. Validation data from PROMMTT database | 10,900/1245 | AUROC (training: 0.956, validation: 0.711) | |
Feng et al. [48] | 2021 | Demand prediction for traumatic blood transfusion | XGBoost | Trauma location, Age, HR, RR, SI, SBP, DBP, SpO2, Temperature | Data from hospital/ER records | 1371 | AUROC: 0.940 |
Lammers et al. [49] | 2022 | Predicting risk of requiring massive Transfusion | RF | HR, RR, DBP, SBP, SpO2, Temperature, INR, Hematocrit, Platelet, pH, mechanism of injury, GCS, AIS, ISS | DoDTR | 22,158 | AUROC: 0.984 |
Chen et al. [50] | 2008 | Determining hypovolemia in patients | Linear ensemble classifiers | HR, RR, DBP, SBP, SpO2 | Data from hospital/ER records | 898 | Accuracy: 0.760 |
Convertino et al. [51] | 2011 | Determining patients at greatest risk of ongoing hemorrhagic shock | undefined ML algorithm | SBP, DBP, RR, blood pH, base deficit | Data from subjects under LBNP | 190 | Accuracy: 0.965 |
Rickards et al. [52] | 2015 | Determining hypovolemia in patients | undefined ML algorithm | HR, stroke volume, ECG, heat flux, skin temperature | Data from subjects through various exercises under LBNP | 24 | Accuracy: 0.926 |
Davis et al. [53] | 2022 | Intracranial hemorrhage detection | NLP tool | CT scan images | Data from hospital/ER records | 200 scans (25,658 images) | Precision: 0.730 |
2020, 2021 | Intracranial hemorrhage detection | NN software | CT scan images | Data from hospital/ER records | 8723 scans | Accuracy: 0.965 | |
Davuluri et al. [56] | 2012 | Hemorrhage detection and image segmentation model | SVM | CT scan images | Data collected from hospital/ER records | 12 scans (515 images) | Accuracy: 0.943 |
Perkins et al. [57] | 2021 | Prediction tool for detecting TIC | BN | HR, SBP, temperature, hemothorax, FAST scan, GCS, lactate, pH, mechanism of injury, fracture assessment | Data from hospital/ER records | 1091 | AUROC: 0.930 |
Li et al. [58] | 2020 | Prediction model for acute traumatic coagulopathy | RF | RBC count, SI, base excess, lactate, DBP, pH | Emergency Rescue Database | 1014 | AUROC: 0.830 |