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Pre-Triage Decision Support Improvement in Maternity Care by Means of Data Mining
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Author(s): Eliana Pereira (University of Minho, Portugal), Andreia Brandão (University of Minho, Portugal), Maria Salazar (Centro Hospitalar do Porto, Portugal), Carlos Filipe Portela (University of Minho, Portugal), Manuel Filipe Santos (University of Minho, Portugal), José Machado (University of Minho, Portugal), António Abelha (University of Minho, Portugal)and Jorge Braga (Centro Hospitalar do Porto, Portugal)
Copyright: 2015
Pages: 18
Source title:
Integration of Data Mining in Business Intelligence Systems
Source Author(s)/Editor(s): Ana Azevedo (Algoritmi R&D Center/University of Minho, Portugal & Polytechnic Institute of Porto/ISCAP, Portugal)and Manuel Filipe Santos (Algoritmi R&D Center/University of Minho, Portugal)
DOI: 10.4018/978-1-4666-6477-7.ch009
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Abstract
A triage system aims to make a correct characterization of the condition of patients. Because conventional triage systems like Manchester Triage System (MTS) are not suitable for maternity care, a decision model for pre-triaging patients in emergency (URG) and consultation (ARGO) classes was built and incorporated into a Decision Support System (DSS) implemented in Centro Materno Infantil do Norte (CMIN). Complementarily, DSS produces several indicators to support clinical and management decisions. A recent data analysis revealed a bias in the classification of URG cases. Frequently, cases classified as URG correspond to ARGO. This misclassification has been studied by means of Data Mining (DM) techniques in order to improve the pre-triage model and to discover knowledge for developing a new triage system based on waiting times and on a 5-scale of classes. This chapter presents a kind of sensitivity analysis combining input variables in six scenarios and considering four different DM techniques. CRISP-DM methodology was used to conduct the project.
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