Percorrer por autor "Vieira, Susana M."
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- Ensemble fuzzy models in personalized medicine: Application to vasopressors administrationPublication . Salgado, Cátia M.; Vieira, Susana M.; Mendonça, Luís; Finkelstein, Stan; Sousa, João M.C.Vasopressors administration in intensive care units is a risky surgical procedure that can be associated with infections, especially if done urgently such as in the case of unexpected systemic shock. The early prediction of a patient׳s transition to vasopressor dependence could improve overall outcomes associated with the procedure. Personalized medicine in the ICU encompasses the customization of healthcare on the level of individual patients, with diagnostic tests, monitoring interventions and treatments being fitted to the individual rather than the “average” patient. In this scope, this paper proposes an ensemble fuzzy modeling approach to a classification problem based on subgroups of patients identified by individual characteristics. A fuzzy c-means clustering algorithm was implemented to find subgroups of patients and each subgroup was used to develop a fuzzy model. The final classification of the ensemble fuzzy approach is obtained using two output selection criteria: an a priori decision criterion based on the distance from the cluster centers to the patients׳ characteristics, and an a posteriori decision criterion based on the uncertainty of the model output. The performance of the proposed approach is investigated using a real world clinical database and nine benchmark datasets. The ensemble fuzzy model approach performs better than the single model for the prediction of vasopressors administration in the ICU, being the a posteriori approach the best performer, with an average AUC of 0.85, showing this way the advantage of a personalized approach for patient care in the ICU.
- Ensemble fuzzy models in personalized medicine: Application to vasopressors administrationPublication . Salgado M., Cátia; Vieira, Susana M.; Mendonça, L. F.; Finkelstein, Stan; Sousa, João M.C.Vasopressors administration in intensive care units is a risky surgical procedure that can be associated with infections, especially if done urgently such as in the case of unexpected systemic shock. The early prediction of a patient׳s transition to vasopressor dependence could improve overall outcomes associated with the procedure. Personalized medicine in the ICU encompasses the customization of healthcare on the level of individual patients, with diagnostic tests, monitoring interventions and treatments being fitted to the individual rather than the “average” patient. In this scope, this paper proposes an ensemble fuzzy modeling approach to a classification problem based on subgroups of patients identified by individual characteristics. A fuzzy c-means clustering algorithm was implemented to find subgroups of patients and each subgroup was used to develop a fuzzy model. The final classification of the ensemble fuzzy approach is obtained using two output selection criteria: an a priori decision criterion based on the distance from the cluster centers to the patients׳ characteristics, and an a posteriori decision criterion based on the uncertainty of the model output. The performance of the proposed approach is investigated using a real world clinical database and nine benchmark datasets. The ensemble fuzzy model approach performs better than the single model for the prediction of vasopressors administration in the ICU, being the a posteriori approach the best performer, with an average AUC of 0.85, showing this way the advantage of a personalized approach for patient care in the ICU.
- Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patientsPublication . Vieira, Susana M.; Mendonça, Luís F. Mendonça; Farinha, Gonçalo J.; Sousa, João Miguel da CostaThis paper proposes a modified binary particle swarm optimization (MBPSO) method for feature election with the simultaneous optimization of SVM kernel parameter setting, applied to mortality prediction in septic patients. An enhanced version of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm is proposed. MBPSO control the swarm variability using the velocity and the similarity between best swarm solutions. This paper uses support vector machines in a wrapper approach, where the kernel parameters are optimized at the same time. The approach is applied to predict the outcome (survived or deceased) of patients with septic shock. Further, MBPSO is tested in several benchmark datasets and is compared with other PSO based algorithms and genetic algorithms (GA). The experimental results showed that the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy, specially when compared to other PSO based algorithms. When compared to GA, MBPSO is similar in terms of accuracy, but the subset solutions have less selected features.