Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–40.
Morales EF, Escalante HJ. Chapter 6—a brief introduction to supervised, unsupervised, and reinforcement learning. In: Torres-García AA, Reyes-García CA, Villaseñor-Pineda L, Mendoza-Montoya O, editors. Biosignal processing and classification using computational learning and intelligence. Academic Press; 2022. p. 111–29.
Singh A, Thakur N, Sharma A (eds). A review of supervised machine learning algorithms. In: 2016 3rd international conference on computing for sustainable global development (INDIACom); 2016. p. 1310–5.
Zou J, Han Y, So S-S. Overview of artificial neural networks. In: Livingstone DJ, editor. Artificial neural networks: methods and applications. Totowa: Humana Press; 2009. p. 14–22.
Moore L, Evans D, Hameed S, Yanchar N, Stelfox H, Simons R, et al. Mortality in Canadian trauma systems: a multicenter cohort study. Ann Surg. 2017;265(1):212–7.
Evans DC. From trauma care to injury control: a people’s history of the evolution of trauma systems in Canada. Can J Surg. 2007;50(5):364–9.
PubMed PubMed Central Google Scholar
Abdel-Aty MA, Abdelwahab HT. Predicting injury severity sevels in traffic crashes: a modeling comparison. J Transp Eng. 2004;130(2):204–10.
AlMamlook RE, Kwayu KM, Alkasisbeh MR, Frefer AA. Comparison of machine learning algorithms for predicting traffic accident severity. In: IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT); 2019. 272–6. https://doi.org/10.1109/JEEIT.2019.8717393.
Amiri AM, Sadri A, Nadimi N, Shams M. A comparison between artificial neural network and hybrid intelligent genetic algorithm in predicting the severity of fixed object crashes among elderly drivers. Accid Anal Prev. 2020;138:105468.
Assi K, Rahman SM, Mansoor U, Ratrout N. Predicting crash injury severity with machine learning algorithm synergized with clustering technique: a promising protocol. Int J Environ Res. 2020;17(15):5497.
Assi K. Prediction of traffic crash severity using deep neural networks: a comparative study. In: International conference on innovation and intelligence for informatics, computing and technologies (3ICT); 2020. p. 1–6. https://doi.org/10.1109/3ICT51146.2020.9311974.
Bao J, Liu P, Ukkusuri SV. A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data. Accid Anal Prev. 2019;122:239–54.
Delen D, Sharda R, Bessonov M. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accid Anal Prev. 2006;38(3):434–44.
Elamrani Abou Elassad Z, Mousannif H, Al Moatassime H. Class-imbalanced crash prediction based on real-time traffic and weather data: a driving simulator study. Traffic Inj Prev. 2020;21(3):201–8.
Iranitalab A, Khattak A. Comparison of four statistical and machine learning methods for crash severity prediction. Accid Anal Prev. 2017;108:27–36.
Mansoor U, Ratrout NT, Rahman SM, Assi K. Crash severity prediction using two-layer ensemble machine learning model for proactive emergency management. IEEE Access. 2020;8:210750–62.
Taamneh S, Taamneh MM. A machine learning approach for building an adaptive, real-time decision support system for emergency response to road traffic injuries. Int J Inj Control Saf Promot. 2021;28(2):222–32.
DiRusso S, Sullivan T, Holly C, Cuff S, Savino J. An artificial neural network as a model for prediction of survival in trauma patients: validation for a regional trauma area. J Trauma. 2000;49(2):212–23.
Article CAS PubMed Google Scholar
Kang DY, Cho KJ, Kwon O, Kwon JM, Jeon KH, Park H, et al. Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services. Scand J Trauma Resusc Emerg Med. 2020;28(1):17.
Article PubMed PubMed Central Google Scholar
Kim D, You S, So S, Lee J, Yook S, Jang DP, et al. A data-driven artificial intelligence model for remote triage in the prehospital environment. PLoS ONE. 2018;13(10):e0206006.
Article PubMed PubMed Central Google Scholar
Liu NT, Holcomb JB, Wade CE, Batchinsky AI, Cancio LC, Darrah MI, et al. Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients. Med Biol Eng Comput. 2014;52(2):193–203.
Nederpelt CJ, Mokhtari AK, Alser O, Tsiligkaridis T, Roberts J, Cha M, et al. Development of a field artificial intelligence triage tool: confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds. J Trauma Acute Care Surg. 2021;90(6):1054–60.
Dennis BM, Stonko DP, Callcut RA, Sidwell RA, Stassen NA, Cohen MJ, et al. Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: a multicenter study. J Trauma Acute Care Surg. 2019;87(1):181–7.
Article PubMed PubMed Central Google Scholar
Menke NB, Caputo N, Fraser R, Haber J, Shields C, Menke MN. A retrospective analysis of the utility of an artificial neural network to predict ED volume. Am J Emerg Med. 2014;32(6):614–7.
Rauch J, Hübner U, Denter M, Babitsch B. Improving the prediction of emergency department crowding: a time series analysis including road traffic flow. Stud Health Technol Inform. 2019;260:57–64.
Stonko DP, Dennis BM, Betzold RD, Peetz AB, Gunter OL, Guillamondegui OD. Artificial intelligence can predict daily trauma volume and average acuity. J Trauma Acute Care Surg. 2018;85(2):393–7.
Batchinsky AI, Salinas J, Jones JA, Necsoiu C, Cancio LC. Predicting the need to perform life-saving interventions in trauma patients by using new vital signs and artificial neural networks. In: Combi C, Shahar Y, Abu-Hanna A, editors. Artificial intelligence in medicine. Berlin: Springer; 2009. https://doi.org/10.1007/978-3-642-02976-9_55.
Bektaş F, Eken C, Soyuncu S, Kilicaslan İ, Cete Y. Artificial neural network in predicting craniocervical junction injury: an alternative approach to trauma patients. Eur J Emerg Med. 2008;15(6):318–23.
Bertsimas D, Masiakos PT, Mylonas KS, Wiberg H. Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach. J Pediatr Surg. 2019;54(11):2353–7.
Cheng C-Y, Chiu IM, Hsu M-Y, Pan H-Y, Tsai C-M, Lin C-HR. Deep learning assisted detection of abdominal free fluid in Morison’s pouch during focused assessment with sonography in trauma. Front Med. 2021;8:707437.
Dreizin D, Zhou Y, Zhang Y, Tirada N, Yuille AL. Performance of a deep learning algorithm for automated segmentation and quantification of traumatic pelvic hematomas on CT. J Digit Imaging. 2020;33(1):243–51.
Liu NT, Holcomb JB, Wade CE, Darrah MI, Salinas J. Utility of vital signs, heart rate variability and complexity, and machine learning for identifying the need for lifesaving interventions in trauma patients. Shock (Augusta, Ga). 2014;42(2):108–14.
Paydar S, Parva E, Ghahramani Z, Pourahmad S, Shayan L, Mohammadkarimi V, et al. Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence. Chin J Traumatol. 2021;24(1):48–52.
Abujaber A, Fadlalla A, Gammoh D, Abdelrahman H, Mollazehi M, El-Menyar A. Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: machine learning approach. PLoS ONE. 2020;15(7):e0235231.
Article CAS PubMed PubMed Central Google Scholar
Ahmed FS, Ali L, Joseph BA, Ikram A, Ul Mustafa R, Bukhari SAC. A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit. J Trauma Acute Care Surg. 2020;89(4):736–42.
Becalick DC, Coats TJ. Comparison of artificial intelligence techniques with UKTRISS for estimating probability of survival after trauma. UK Trauma and Injury Severity Score. J Trauma. 2001;51(1):123–33.
Article CAS PubMed Google Scholar
Christie SA, Conroy AS, Callcut RA, Hubbard AE, Cohen MJ. Dynamic multi-outcome prediction after injury: applying adaptive machine learning for precision medicine in trauma. PLoS ONE. 2019;14(4):e0213836.
Article CAS PubMed PubMed Central Google Scholar
Demšar J, Zupan B, Aoki N, Wall MJ, Granchi TH, Robert BJ. Feature mining and predictive model construction from severe trauma patient’s data. Int J Med Inform. 2001;63(1):41–50.
DiRusso SM, Chahine AA, Sullivan T, Risucci D, Nealon P, Cuff S, et al., editors. Development of a model for prediction of survival in pediatric trauma patients: comparison of artificial neural networks and logistic regression. J Pediatr Surg. 2002;37(7):1098–104. https://doi.org/10.1053/jpsu.2002.33885.
El Hechi MW, Maurer LR, Levine J, Zhuo D, El Moheb M, Velmahos GC, et al. Validation of the artificial intelligence-based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) calculator in emergency general surgery and emergency laparotomy patients. J Am Coll Surg. 2021;232(6):912-9.e1.
Gorczyca MT, Toscano NC, Cheng JD. The trauma severity model: an ensemble machine learning approach to risk prediction. Comput Biol Med. 2019;108:9–19.
Hale AT, Stonko DP, Lim J, Guillamondegui OD, Shannon CN, Patel MB. Using an artificial neural network to predict traumatic brain injury. J Neurosurg Pediatr. 2018;23(2):219–26.
Article PubMed PubMed Central Google Scholar
Ji SY, Smith R, Huynh T, Najarian K. A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries. BMC Med Inform Decis Mak. 2009;9:2.
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