Multiclassifier Systems for Predicting Neurological Outcome of Patients with Severe Trauma and Polytrauma in Intensive Care Units.
This paper presents an ensemble based classification proposal for predicting neurological outcome of severely traumatized patients. The study comprises both the whole group of patients and a subgroup containing those patients suffering traumatic brain injury (TBI). Data was gathered from patients ho...
| Publicado en: | Journal of Medical Systems Vol. 41; no. 9; pp. 1 - 9 |
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| Autores principales: | , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2017
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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=125068541&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125068541 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2017 vid: 41 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125068541 125068541 125068541 10.1007/s10916-017-0789-1 125068541 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Multiclassifier Systems for Predicting Neurological Outcome of Patients with Severe Trauma and Polytrauma in Intensive Care Units. aug: au: González-Robledo, Javier Martín-González, Félix Sánchez-Barba, Mercedes Sánchez-Hernández, Fernando Moreno-García, María affil: Intensive Care Unit , University Hospital of Salamanca , Salamanca Spain sug: subj: Trauma Complications Neurologic Examination Outcomes (Health Care) Intensive Care Units Patient Assessment Brain Injuries Diagnosis Classification Patient Care Literature Review APACHE (Acute Physiology and Chronic Health Evaluation) Scales Glasgow Coma Scale Evaluation Data Analysis Software Validation Studies Sensitivity and Specificity Biometrics ab: This paper presents an ensemble based classification proposal for predicting neurological outcome of severely traumatized patients. The study comprises both the whole group of patients and a subgroup containing those patients suffering traumatic brain injury (TBI). Data was gathered from patients hospitalized in the Intensive Care Unit (ICU) of the University Hospital in Salamanca. Predictive models were induced from both epidemiologic and clinical variables taken at the emergency room and along the stay in the ICU. The large number of variables leads to a low accuracy in the classifiers even when feature selection methods are used. In addition, the presence of a much larger number of instances of one of the classes in the subgroup of TBI patients produces a significantly lesser precision for the minority class. Usual ways of dealing with the last problem is to use undersampling and oversampling strategies, which can lead to the loss of valuable data and overfitting problems respectively. Our proposal for dealing with these problems is based in the use of ensemble multiclassifiers as well as in the use of an ensemble playing the role of base classifier in multiclassifiers. The proposed strategy gave the best values of the selected quality measures (accuracy, precision, sensitivity, specificity, F-measure and area under the Receiver Operator Characteristic curve) as well as the closest values of precision for the two classes under study in the case of the classification from imbalanced data. 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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