Improving Prediction Accuracy of “Central Line-Associated Blood Stream Infections” Using Data Mining Models.
Prediction of nosocomial infections among patients is an important part of clinical surveillance programs to enable the related personnel to take preventive actions in advance. Designing a clinical surveillance program with capability of predicting nosocomial infections is a challenging task due to...
| Publicado en: | BioMed Research International Vol. 2017; pp. 1 - 13 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
9/20/2017
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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=125245762&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125245762 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/20/2017 vid: 2017 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 125245762 125245762 125245762 10.1155/2017/3292849 125245762 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Improving Prediction Accuracy of “Central Line-Associated Blood Stream Infections” Using Data Mining Models. aug: au: Noaman, Amin Y. Nadeem, Farrukh Ragab, Abdul Hamid M. Jamjoom, Arwa Al-Abdullah, Nabeela Nasir, Mahreen Ali, Anser G. affil: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia sug: subj: Cross Infection Risk Factors Central Venous Catheters Adverse Effects Data Mining Methods Human Disease Surveillance Research Methodology ab: Prediction of nosocomial infections among patients is an important part of clinical surveillance programs to enable the related personnel to take preventive actions in advance. Designing a clinical surveillance program with capability of predicting nosocomial infections is a challenging task due to several reasons, including high dimensionality of medical data, heterogenous data representation, and special knowledge required to extract patterns for prediction. In this paper, we present details of six data mining methods implemented using cross industry standard process for data mining to predict central line-associated blood stream infections. For our study, we selected datasets of healthcare-associated infections from US National Healthcare Safety Network and consumer survey data from Hospital Consumer Assessment of Healthcare Providers and Systems. Our experiments show that central line-associated blood stream infections (CLABSIs) can be successfully predicted using AdaBoost method with an accuracy up to 89.7%. This will help in implementing effective clinical surveillance programs for infection control, as well as improving the accuracy detection of CLABSIs. Also, this reduces patients’ hospital stay cost and maintains patients’ safety. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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