Extracting the Factors Affecting the Survival Rate of Trauma Patients Using Data Mining Techniques on a National Trauma Registry
Archives of Academic Emergency Medicine,
Vol. 11 No. 1 (2023),
15 November 2022
,
Page e1
https://doi.org/10.22037/aaem.v11i1.1763
Abstract
Introduction: Thousands of people die due to trauma all over the world every day, which leaves adverse effects on families and the society. The main objective of this study was to identify the factors affecting the mortality of trauma patients using data mining techniques.
Methods: The present study includes six parts: data gathering, data preparation, target attributes specification, data balancing, evaluation criteria, and applied techniques. The techniques used in this research are all from the decision tree family. The output of these techniques are patterns extracted from the trauma patients dataset (National Trauma Registry of Iran). The dataset includes information on 25,986 trauma patients from all over the country. The techniques that were used include random forest, CHAID, and ID3.
Results: Random forest performs better than the other two techniques in terms of accuracy. The ID3 technique performs better than the other two techniques in terms of the dead class. The random forest technique has performed better than other techniques in the living class. The rules with the most support, state that if the Injury Severity Score (ISS) is minor and vital signs are normal, 98% of people will survive. The second rule, in terms of support, states that if ISS is minor and vital signs are abnormal, 93% will survive. Also, by increasing the threshold of the patient's arrival time from 10 to 15 minutes, no noticeable difference was observed in the death rate of patients.
Conclusion: Transfer time of less than ten minutes in patietns whose ISS is minor, can increase the chance of survival. Impaired vital signs can decrease the chance of survival in traffic accidents. Also, if the ISS is minor in non-penetrating trauma, regardless of vital signs and if the victim is transported in less than ten minutes, the patient will survive with 99% certainty.
- Data Mining
- Survival
- Mortality
- Trauma Severity Indices
- Injuries
- Injury Severity Score
How to Cite
References
Benjet C, Bromet E, Karam EG, Kessler RC, McLaughlin KA, Ruscio AM, et al. The epidemiology of traumatic event exposure worldwide: results from the World Mental Health Survey Consortium. Psychological medicine(PM). 2016;46(2):327-43.
Nixon RDV, Bryant RA, Moulds ML, Felmingham KL, Mastrodomenico JA. Physiological arousal and dissociation in acute trauma victims during trauma narratives. Journal of Traumatic Stress(JTS(. 2005;18(2):107-13.
Magruder KM, McLaughlin KA, Elmore Borbon DL. Trauma is a public health issue. European Journal of Psychotraumatology(EJP). 2017;8(1):1375338.
Meagher AD, Lin A, Mandell SP, Bulger E, Newgard C. A Comparison of Scoring Systems for Predicting Short‐and Long‐term Survival After Trauma in Older Adults. Academic Emergency Medicine(AEM). 2019;26(6):621-30.
Chaurasia V, Pal S, Tiwari BB. Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms & Computational Technology(JACT). 2018;12(2):119-26.
Amin MS, Chiam YK, Varathan KD. Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics(TI). 2019;36:82-93.
Yadollahi M. A study of mortality risk factors among trauma referrals to trauma center, Shiraz, Iran, 2017. Chinese Journal of Traumatology(CJT). 2019;22(04):212-8.
Hassanzadeh M FAYS. Using Data Mining Techniques to Extract Clinical Disorders Affecting Mortality in Trauma Patients. Guilan Uni Med Sci(GUMS). 2015;24(95):62-52.
Liu NT, Salinas J. Machine learning for predicting outcomes in trauma. Shock: Injury, Inflammation, and Sepsis: Laboratory and Clinical Approaches(LCA). 2017;48(5):504-10.
Rau C-S, Kuo P-J, Chien P-C, Huang C-Y, Hsieh H-Y, Hsieh C-H. Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models. PloS One(PSO). 2018;13(11):e0207192.
González-Robledo J, Martín-González F, Moreno-García M, Sánchez-Barba M, Sánchez-Hernández F. Prognostic factors associated with mortality in patients with severe trauma: from prehospital care to the intensive care unit. Medicina Intensiva(MI) (English Edition). 2015;39(7):412-21.
Arora J, Bhalla N, Rao S. A review on association rule mining algorithms. International Journal of Innovative Research in Computer and Communication Engineering(IJIRCCE). 2013;1(5):1246-51.
Champion HR, Copes WS, Sacco WJ, Lawnick MM, Keast SL, Frey CF. The Major Trauma Outcome Study: establishing national norms for trauma care. Journal of Trauma and Acute Care Surgery(JTACS(. 1990;30(11):1356-65.
Kuo SCH, Kuo P-J, Chen Y-C, Chien P-C, Hsieh H-Y, Hsieh C-H. Comparison of the new Exponential Injury Severity Score with the Injury Severity Score and the New Injury Severity Score in trauma patients: A cross-sectional study. PloS One(PSO). 2017;12(11):e0187871.
Lovely R, Trecartin A, Ologun G, Johnston A, Svintozelskiy S, Vermeylen F, et al. Injury Severity Score alone predicts mortality when compared to EMS scene time and transport time for motor vehicle trauma patients who arrive alive to hospital. Traffic Injury Prevention(TIP). 2018;19(sup2):S167-S8.
Chong S-L, Liu N, Barbier S, Ong MEH. Predictive modeling in pediatric traumatic brain injury using machine learning. BMC Medical Research Methodology(BMRM). 2015;15(1):1-9.
Tien HCN, Jung V, Pinto R, Mainprize T, Scales DC, Rizoli SB. Reducing time-to-treatment decreases mortality of trauma patients with acute subdural hematoma. Annals of Surgery(AS). 2011;253(6):1178-83.
McNicholl BP. The golden hour and prehospital trauma care. Injury. 1994;25(4):251-4.
Murdock DB. Trauma: when there's no time to count. Association of periOperative Registered Nurses Journal(AORNJ. 2008;87(2):322-8.
Hoyt DB, Coimbra R, Potenza BM. Trauma systems, triage, and transport. Trauma. 2008;6:57-82.
Williamson K, Ramesh R, Grabinsky A. Advances in prehospital trauma care. International Journal of Critical Illness and Injury Science(IJCIIS(. 2011;1(1):44.
Feero S, Hedges JR, Simmons E, Irwin L. Does out-of-hospital EMS time affect trauma survival? The American Journal of Emergency Medicine(AJEM). 1995;13(2):133-5.
Mitchell AD, Tallon JM, Sealy B. Air versus ground transport of major trauma patients to a tertiary trauma centre: a province-wide comparison using TRISS analysis. Canadian Journal of Surgery(CJS). 2007;50(2):129.
- Abstract Viewed: 453 times
- pdf Downloaded: 892 times