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- Marketing database knowledge extraction - towards a domain ontologyPublication . Pinto, Filipe Mota; Gago, Pedro; Santos, Manuel Filipe; Mota Pinto, Filipe; Gago, PedroOntologies are currently the most prominent computer science research area under development. With this paper we use ontologies at an almost unexplored research area within the marketing discipline, throughout ontological approach to the database marketing. We propose a generic framework supported by ontologies for the knowledge extraction from marketing databases. Therefore this work has two purposes: to integrate ontological approach in Database Marketing and to create domain ontology with a knowledge base that will enhance the entire process at both levels: marketing and knowledge extraction techniques. This research was developed according to two methodological principles, ontology domain double articulation and ontology modularization. At the end of our work we use ontologies to pre-generalize the Database Marketing knowledge through a knowledge base.
- INTCare: On-line knowledge discovery in the intensive care unitPublication . Gago, Pedro; Fernandes, C.; Pinto, Filipe; Santos, M.F.In our work aim to automate the knowledge discovery process. In this paper we present the INTCare system, an intelligent decision support system for intensive care medicine. INTCare is an agent based system that has (autonomous) agents responsible both for data acquisition and model updating thus reducing the need for human intervention. In the present, INTCare is predicting organ failure and probability of in-hospital death. Reliable prediction results facilitate a change from the current reactive behavior to a pro-active one thus enhancing the quality of service. The functional and structural aspects are presented as are some results obtained using data collected from the bedside monitors.
- Adaptive knowledge discovery for decision support in intensive care unitsPublication . Gago, Pedro; Santos, Manuel FilipeClinical Decision Support Systems (CDSS) are becoming commonplace. They are used to alert doctors about drug interactions, to suggest possible diagnostics and in several other clinical situations. One of the approaches to building CDSS is by using techniques from the Knowledge Discovery from Databases (KDD) area. However using KDD for the construction of the knowledge base used in such systems, while reducing the maintenance work still demands repeated human intervention. In this work we present a KDD based architecture for CDSS for intensive care medicine. By resorting to automated data acquisition our architecture allows for the evaluation of the predictions made and subsequent action aiming at improving the predictive performance thus enhancing adaptive capacities.
- Closed loop knowledge discovery for decision support in intensive care medicinePublication . Gago, Pedro; Santos, Manuel FilipeClinical Decision Support Systems (CDSS) are becoming commonplace. They are used to alert doctors about drug interactions, to suggest possible diagnostics and in several other clinical situations. One of the approaches to building CDSS is by using techniques from the Knowledge Discovery from Databases (KDD) area. However using KDD for the construction of the knowledge base used in such systems, while reducing the maintenance work still demands repeated human intervention. In this work we present a KDD based architecture for CDSS for intensive care medicine. By resorting to automated data acquisition our architecture allows for the evaluation of the predictions made and subsequent action aiming at improving the predictive performance thus closing the KDD loop.
- Evaluating Hybrid Ensembles for Intelligent Decision Support for Intensive CarePublication . Gago, Pedro; Santos, Manuel FilipeThe huge amount of data available in an Intensive Care Unit (ICU) makes ICUs an attractive field for data analysis. However, effective decision support systems operating in such an environment should not only be accurate but also as autonomous as possible, being capable of maintaining good performance levels without human intervention. Moreover, the complexity of an ICU setting is such that available data only manages to cover a limited part of the feature space. Such characteristics led us to investigate the development of ensemble update techniques capable of improving the discriminative power of the ensemble. Our chosen technique is inspired by the Dynamic Weighted Majority algorithm, an algorithm initially developed for the concept drift problem. In this paper we will show that in the problem we are addressing, simple weight updates do not improve results, whereas an ensemble, where we allow not only weight updates, but also the creation and eliminations of models, significantly increases classification performance.