IN-HOSPITAL MORTALITY PREDICTION IN PATIENTS RECEIVING MECHANICAL VENTILATION IN TAIWAN.
Background: Few studies have used pooled data for more than 2 years and few have analyzed data for patients receiving mechanical ventilation in Taiwan. Objective: To validate the use of an artificial neural network model for predicting in-hospital mortality in patients receiving mechanical ventilati...
| Publicado en: | American Journal of Critical Care Vol. 22; no. 6; pp. 506 - 514 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
American Association of Critical-Care Nurses
Nov2013
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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=104150922&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104150922 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10623264 44L jtl: American Journal of Critical Care issn: 10623264 maglogo: N pubinfo: dt: Nov2013 vid: 22 iid: 6 pid: 2559 pub: American Association of Critical-Care Nurses place: Alisa Veijo, California artinfo: ui: 104150922 91733300 10.4037/ajcc2013950 NLM24186822 104150922 ppf: 506 ppct: 8 formats: fmt: @attributes: type: P tig: atl: IN-HOSPITAL MORTALITY PREDICTION IN PATIENTS RECEIVING MECHANICAL VENTILATION IN TAIWAN. aug: au: Chao-Ju Chen Hon-Yi Shi King-Teh Lee Tzuu-Yuan Huang affil: Head, Department of Respiratory Therapy, Madou Sin-Lau Hospital, Tainan, Taiwan sug: subj: Respiration, Artificial Taiwan Hospital Mortality Taiwan Ventilator Patients Taiwan Neural Networks (Computer) Logistic Regression Human ROC Curve T-Tests Validation Studies Record Review Databases International Classification of Diseases Length of Stay Scales One-Way Analysis of Variance Age Factors Sex Factors Fisher's Exact Test Sensitivity and Specificity Chi Square Test Male Female Middle Age Aged Aged, 80 and Over Predictive Research Odds Ratio Confidence Intervals Hospitals Classification Predictive Value of Tests Effect Size Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Background: Few studies have used pooled data for more than 2 years and few have analyzed data for patients receiving mechanical ventilation in Taiwan. Objective: To validate the use of an artificial neural network model for predicting in-hospital mortality in patients receiving mechanical ventilation in Taiwan and to compare the predictive accuracy of the artificial neural network model with that of a logistic regression model. Methods: Retrospective comparison of 1000 pairs of data sets processed by logistic regression and artificial neural network models based on initial clinical data for 213 945 patients receiving mechanical ventilation. For each pair of artificial neural network and logistic regression models, the area under the receiver operating characteristic curves, Hosmer-Lemeshow statistics, and accuracy rate were calculated and compared by using t tests. Global sensitivity analysis and sensitivity score approach were also used to assess the relative significance of input parameters in the system model and the relative importance of variables. Results: Compared with the logistic regression model, the artificial neural network model had a better accuracy rate in 96.3% of cases, better Hosmer-Lemeshow statistics in 41.2% of cases, and a better area under the curve in 97.6% of cases. Hospital volume was the most influential (sensitive) variable affecting in-hospital mortality, followed by Charlson comorbidity index, length of stay, and hospital type. Conclusions: Compared with the conventional logistic regression model, the artificial neural network model was more accurate in predicting in-hospital mortality and had higher overall performance indices. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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