Improving Prediction Accuracy of 'Central Line-Associated Blood Stream Infections' Using Data Mining Models
Autor: | Farrukh Nadeem, Anser G. Ali, Amin Y. Noaman, Nabeela Al-Abdullah, Mahreen Nasir, Arwa Jamjoom, Abdul Hamid M. Ragab |
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Rok vydání: | 2017 |
Předmět: |
Databases
Factual Article Subject MEDLINE lcsh:Medicine 030501 epidemiology External Data Representation computer.software_genre General Biochemistry Genetics and Molecular Biology Task (project management) 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests Health care Data Mining Humans Infection control Medicine 030212 general & internal medicine AdaBoost Central line General Immunology and Microbiology business.industry lcsh:R General Medicine United States Catheter-Related Infections Cross Industry Standard Process for Data Mining Data mining 0305 other medical science business computer Research Article |
Zdroj: | BioMed Research International BioMed Research International, Vol 2017 (2017) |
ISSN: | 2314-6141 2314-6133 |
Popis: | 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. |
Databáze: | OpenAIRE |
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