Survival prediction of trauma patients: a study on US National Trauma Data Bank.
Background: Exceptional circumstances like major incidents or natural disasters may cause a huge number of victims that might not be immediately and simultaneously saved. In these cases it is important to define priorities avoiding to waste time and resources for not savable victims. Trauma and Inju...
| Publicado en: | European Journal of Trauma & Emergency Surgery Vol. 43; no. 6; pp. 805 - 823 |
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| Autores principales: | , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=126461499&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 126461499 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18639933 3C05 jtl: European Journal of Trauma & Emergency Surgery issn: 18639933 maglogo: N pubinfo: dt: Dec2017 vid: 43 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 126461499 126461499 144117465 126461499 10.1007/s00068-016-0757-3 126461499 ppf: 805 ppct: 18 formats: fmt: @attributes: type: P tig: atl: Survival prediction of trauma patients: a study on US National Trauma Data Bank. aug: au: Sefrioui, I. Amadini, R. Mauro, J. El Fallahi, A. Gabbrielli, M. affil: Faculty of Sciences of Tetouan , University Abdelmalek Essaadi , Tétouan Morocco sug: subj: Trauma Emergency Patients Survival Outcomes (Health Care) Human Clinical Assessment Tools Comparative Studies Decision Trees Sensitivity and Specificity kappa Statistic ROC Curve Machine Learning ab: Background: Exceptional circumstances like major incidents or natural disasters may cause a huge number of victims that might not be immediately and simultaneously saved. In these cases it is important to define priorities avoiding to waste time and resources for not savable victims. Trauma and Injury Severity Score (TRISS) methodology is the well-known and standard system usually used by practitioners to predict the survival probability of trauma patients. However, practitioners have noted that the accuracy of TRISS predictions is unacceptable especially for severely injured patients. Thus, alternative methods should be proposed. Methods: In this work we evaluate different approaches for predicting whether a patient will survive or not according to simple and easily measurable observations. We conducted a rigorous, comparative study based on the most important prediction techniques using real clinical data of the US National Trauma Data Bank. Results: Empirical results show that well-known Machine Learning classifiers can outperform the TRISS methodology. Based on our findings, we can say that the best approach we evaluated is Random Forest: it has the best accuracy, the best area under the curve, and k-statistic, as well as the second-best sensitivity and specificity. It has also a good calibration curve. Furthermore, its performance monotonically increases as the dataset size grows, meaning that it can be very effective to exploit incoming knowledge. Considering the whole dataset, it is always better than TRISS. Finally, we implemented a new tool to compute the survival of victims. This will help medical practitioners to obtain a better accuracy than the TRISS tools. Conclusion: Random Forests may be a good candidate solution for improving the predictions on survival upon the standard TRISS methodology. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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