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Projeto de investigação
INTCARE - Intelligent Decision Support System for Intensive Care
Financiador
Autores
Publicações
Evaluating Hybrid Ensembles for Intelligent Decision Support for Intensive Care
Publication . Gago, Pedro; Santos, Manuel Filipe
The 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.
INTCare: On-line knowledge discovery in the intensive care unit
Publication . 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 units
Publication . Gago, Pedro; Santos, Manuel Filipe
Clinical 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 medicine
Publication . Gago, Pedro; Santos, Manuel Filipe
Clinical 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.
Pervasive Information Systems to Intensive Care Medicine. Technology Acceptance Model
Publication . Aguiar, Jorge; Portela, Filipe; Santos, Manuel Filipe; Machado, José; Abelha, António; Silva, Álvaro; Rua, Fernando; Pinto, Filipe
The usability of information systems in critical environments like Intensive Care Units (ICU) is far than the expected and desirable. Typically, ICUs have a set of not integrated information silos and a high number of data recorded in paper. Whenever ICU professionals need to make a decision they have to deal with a high number of data sources containing useful information. Unfortunately, they can't use those sources due to the difficulty of evaluating them in a correct time. Pervasive Intelligent Decision Support Systems (PIDSS), operating automatically and in real-time, can be used to improve the decision making if they are suited to the requirements of the ICU. In this work a PIDSS have been assessed in terms of quality and user acceptance making use of Technology Acceptance Model (TAM). TAM proved to be very useful when combined with Delphi method features to involve the professionals and to make the system usable.
Unidades organizacionais
Descrição
Palavras-chave
Intelligent Decision Support Systems,Adapative Systems,Intelligent Systems,Medicina Intensiva, Exact sciences ,Exact sciences/Computer and information sciences
Contribuidores
Financiadores
Entidade financiadora
Fundação para a Ciência e a Tecnologia, I.P.
Fundação para a Ciência e a Tecnologia, I.P.
Fundação para a Ciência e a Tecnologia, I.P.
Programa de financiamento
5876-PPCDTI
Concurso para Projectos de I&D em todos os Domínios Científicos - 2006
Concurso para Projectos de I&D em todos os Domínios Científicos - 2006
Número da atribuição
PTDC/EIA/72819/2006