Predicting Long-Term Outcome After Traumatic Brain Injury Using Repeated Measurements of Glasgow Coma Scale and Data Mining Methods.
Previous studies have identified some clinical parameters for predicting long-term functional recovery and mortality after traumatic brain injury (TBI). Here, data mining methods were combined with serial Glasgow Coma Scale (GCS) scores and clinical and laboratory parameters to predict 6-month funct...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 2; pp. 1 - 11 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2015
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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=115925395&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925395 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2015 vid: 39 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925395 115925395 115925395 10.1007/s10916-014-0187-x 115925395 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Predicting Long-Term Outcome After Traumatic Brain Injury Using Repeated Measurements of Glasgow Coma Scale and Data Mining Methods. aug: au: Lu, Hsueh-Yi Li, Tzu-Chi Tu, Yong-Kwang Tsai, Jui-Chang Lai, Hong-Shiee Kuo, Lu-Ting affil: Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Douliou City 640 Taiwan sug: subj: Brain Injuries Prognosis Outcome Assessment Methods Data Mining Methods Glasgow Coma Scale Utilization Outcomes (Health Care) Mortality Human Repeated Measures Retrospective Design Scales Neural Networks (Computer) Logistic Regression Decision Trees Academic Medical Centers Taiwan Intraclass Correlation Coefficient Descriptive Statistics Data Analysis Software ROC Curve T-Tests Middle Age Adult Aged Middle Aged: 45-64 years Adult: 19-44 years Aged: 65+ years ab: Previous studies have identified some clinical parameters for predicting long-term functional recovery and mortality after traumatic brain injury (TBI). Here, data mining methods were combined with serial Glasgow Coma Scale (GCS) scores and clinical and laboratory parameters to predict 6-month functional outcome and mortality in patients with TBI. Data of consecutive adult patients presenting at a trauma center with moderate-to-severe head injury were retrospectively analyzed. Clinical parameters including serial GCS measurements at emergency department, 7th day, and 14th day and laboratory data were included for analysis ( n = 115). We employed artificial neural network (ANN), naïve Bayes (NB), decision tree, and logistic regression to predict mortality and functional outcomes at 6 months after TBI. Favorable functional outcome was achieved by 34.8 % of the patients, and overall 6-month mortality was 25.2 %. For 6-month functional outcome prediction, ANN was the best model, with an area under the receiver operating characteristic curve (AUC) of 96.13 %, sensitivity of 83.50 %, and specificity of 89.73 %. The best predictive model for mortality was NB with AUC of 91.14 %, sensitivity of 81.17 %, and specificity of 90.65 %. Sensitivity analysis demonstrated GCS measurements on the 7th and 14th day and difference between emergency room and 14th day GCS score as the most influential attributes both in mortality and functional outcome prediction models. Analysis of serial GCS measurements using data mining methods provided additional predictive information in relation to 6-month mortality and functional outcome in patients with moderate-to-severe TBI. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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