بررسی اختلال پاراکلينيک موثر در مرگومير بيماران ترومايی با استفاده از شيوههای داده کاوی
Introduction: Trauma is one of the most common causes of death in the world, which often occurs as a result of road accidents. Prompt identification of patients with acute injury, leads to take the appropriate medical actions and thus, save lives and also avoid enormous cost of treatment. Objective:...
| Publicado en: | Journal of Guilan University of Medical Sciences Vol. 24; no. 95; pp. 52 - 63 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Journal of Guilan University of Medical Sciences
2015
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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=110877065&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110877065 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20084048 EVAI jtl: Journal of Guilan University of Medical Sciences issn: 20084048 maglogo: N pubinfo: dt: 2015 vid: 24 iid: 95 pid: 76615 pub: Journal of Guilan University of Medical Sciences artinfo: ui: 110877065 110877065 110877065 110877065 ppf: 52 ppct: 11 formats: tig: atl: بررسی اختلال پاراکلينيک موثر در مرگومير بيماران ترومايی با استفاده از شيوههای داده کاوی aug: au: حسنزاده, مريم فرهودینژاد, اکبر يوسفزاده, شاهرخ affil: گروه مهندسى كتمبيتور و فناورى اطلاعات، دانشكده فنى مهندسى، دانشگاه پيام نور، تهران، ايران sug: ab: Introduction: Trauma is one of the most common causes of death in the world, which often occurs as a result of road accidents. Prompt identification of patients with acute injury, leads to take the appropriate medical actions and thus, save lives and also avoid enormous cost of treatment. Objective: Finding the best data mining algorithms to identify clinical disorders resulting in death in trauma patients Materials and Methods: 1,073 trauma patients hospitalized in Poursina Hospital in Rasht with their 52 recorded clinical conditions (features) have been analyzed in this research. In order to automatically identify emergency cases, a number of classification algorithms have been modified for the task, such as decision tree, K-nearest neighbor, and neural network methods. These algorithms have been trained over a wide range of features and their performance has been investigated using 10-fold cross validation. Results: Totally, 82.8% (888) of the surveyed patients were male and17.2% (185) were female. 22.1% died, most of them (30%) in the first week after their hospitalization and 23.6% on the first day. No significant relationship has been found between the duration of hospitalization and mortality. Among the classification algorithms, decision tree and k-nearest were able to recognize death cases with higher precision, (i.e. 91% and 89%, respectively). In order to find effective factors on training a better Decision Tree classifier, the Best First algorithm was used which then selected and could identify 18 effective features (of 52 initial features). Conclusion: Given the high accuracy of some data mining algorithms, like Decision Tree algorithm, we are able to differentiate severe trauma cases which may lead to death from those with mild injuries. Hence, their application to predict mortality in trauma patients and identify those at life risk can be investigated in real environment. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: Persian refInfo: holdings: @attributes: islocal: N |
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