... João ; Juste Ruiz José ; Badenes Casino Margarita ; Thomas Urs P. ; Bell Stuart ; Hertoghs Ma... more ... João ; Juste Ruiz José ; Badenes Casino Margarita ; Thomas Urs P. ; Bell Stuart ; Hertoghs Marleen ; Garabello Roberta ; Seršić Maja ; Wajda Stanislaw ; Turgut Nükhet ; Bogdanović Slavko ; Vinogradov Sergei ; Sorokina Olga ; Hirsch Moshe ; Sobel Lauren ; Rosencranz Armin ...
Communications in Computer and Information Science, 2010
Page 1. Predicting Outcomes of Septic Shock Patients Using Feature Selection Based on Soft Comput... more Page 1. Predicting Outcomes of Septic Shock Patients Using Feature Selection Based on Soft Computing Techniques * André S. Fialho 1,2,3 , Federico Cismondi 1,2,3 , Susana M. Vieira 1,3 , Joao MC Sousa 1,3 , Shane R. Reti ...
IEEE International Conference on Fuzzy Systems, 2013
ABSTRACT We propose the application of probabilistic fuzzy systems (PFS) to model the prediction ... more ABSTRACT We propose the application of probabilistic fuzzy systems (PFS) to model the prediction of early readmission in intensive care unit patients and compare it with the gold-standard method - logistic regression based on the APACHE II score. PFS are characterized by the combination of the linguistic description of the system with the statistical properties of data. On one hand, results point that PFS models perform comparably to the gold-standard method, with AUC values of 0.66±0.03. On the other hand, results also show that PFS models use a significant lower number of variables which, from the clinical practice point of view, suggests improved gains in terms of simplicity.
IEEE International Conference on Fuzzy Systems, 2012
ABSTRACT In the present work, we propose the application of probabilistic fuzzy systems (PFS) to ... more ABSTRACT In the present work, we propose the application of probabilistic fuzzy systems (PFS) to model the prediction of mortality in septic shock patients. This technique is characterized by the combination of the linguistic description of the system with the statistical properties of data. Preliminary results for this particular clinical problem point that PFS models, besides performing as accurately as first order Takagi-Sugeno fuzzy models, also provide probability measures that provide additional clinical information upon which physicians can act on.
2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013
ABSTRACT This paper presents a proposal for a decision support system for the prevention of Inten... more ABSTRACT This paper presents a proposal for a decision support system for the prevention of Intensive Care Unit readmissions, that is being built based on data from the MIMIC II database. The proposed system fuses the results of two distinct classification approaches, one based on available numerical data, and the other based on medical text annotations. The former uses neural fuzzy models and fuzzy modeling in order to provide interpretable classification results. The latter is based on the creation of top-k edited word lists and fuzzy fingerprints. Model fusion is accomplished using a two-tiered fuzzy rule based architecture.
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013
ABSTRACT Shock is a life-threatening medical condition requiring the administration of powerful d... more ABSTRACT Shock is a life-threatening medical condition requiring the administration of powerful drugs - vasopressors. Early identification of these patients is a worthy goal in order to timely prepare them for therapy. A subset composed of the most frequently sampled and readily available variables in an intensive care unit (ICU) was used for clustering patients. Then, a data exploration process was started through the use of fuzzy clustering with the fuzzy cmeans algorithm, where four clusters were obtained and the groups characteristics were analyzed. A relationship between the clusters obtained and the use of vasopressors was found out and these results were visualized with the help of histograms. First, a single model was derived. Then, four models were trained and used for a multi model approach, one for each identified group of patients. In both cases fuzzy models were used as they are universal approximators. For the multi-model approach, two decision criteria were used. First a decision a priori based on the distance from the clusters centers to the patient characteristics was used. Lastly a decision a posteriori approach where each model was used and the final outcome used is based on the uncertainty of the output response to the threshold of each model. The multi model approach with a posteriori decision had a better performance of the two schemes tested, and also performed better than the single general model approach.
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012
ABSTRACT The amount of data generated in the intensive care environment nowadays prohibits the st... more ABSTRACT The amount of data generated in the intensive care environment nowadays prohibits the storage of all the information available. The validation process is time consuming, since nurses have to check every certain periods the data acquired from bedside monitors in order to assess their validity and integrity. This work presents an automatic method for data validation in the intensive care environment, based on an artificial intelligence approach, namely artificial neural networks (ANNs). A real world dataset acquired at Beth Israel Deaconess Medical Center (BIDMC) neonatal intensive care unit (NICU) is used to obtain the validation model and assess its performance. The dataset consists of high frequency sampled data of the level of oxygen saturation (SpO2) of neonates. A subset of 100 neonates was considered for modeling purposes. A total of 7,018,662 samples were available, containing 129,075 validated ones. The performance of the validation model, assessed in terms of its AUC, was of up to 0.75. Both the sensitivity and specificity reached acceptable values according to medical review. Future work would involve a prospective study and validation of the methods proposed in this work.
2012 IEEE Congress on Evolutionary Computation, 2012
ABSTRACT This paper proposes the application of a new binary particle swarm optimization (BPSO) m... more ABSTRACT This paper proposes the application of a new binary particle swarm optimization (BPSO) method to feature selection problems. Two enhanced versions of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm, are proposed. These methods control the swarm variability using the velocity and the similarity between best swarm solutions. The proposed PSO methods use neural networks, fuzzy models and support vector machines in a wrapper approach, and are tested in a benchmark database. It was shown that the proposed BPSO approaches require an inferior simulation time, less selected features and increase accuracy. The best BPSO is then compared with genetic algorithms (GA) and applied to a real medical application, a sepsis patient database. The objective is to predict the outcome (survived or deceased) of the sepsis patients. It was shown that the proposed BPSO approaches are similar in terms of model accuracy when compared to GA, while requiring an inferior simulation time and less selected features.
2006 IEEE International Conference on Fuzzy Systems, 2006
... results The fault accommodation is made considering the three outputs of container gantry cra... more ... results The fault accommodation is made considering the three outputs of container gantry crane: horizontal position ... Table II shows the control results when the fault F1 occurs, using algorithm for ... sum squared error between the references and the outputs of the system after the ...
To compare general and disease-based modeling for fluid resuscitation and vasopressor use in inte... more To compare general and disease-based modeling for fluid resuscitation and vasopressor use in intensive care units. Retrospective cohort study involving 2944 adult medical and surgical intensive care unit (ICU) patients receiving fluid resuscitation. Within this cohort there were two disease-based groups, 802 patients with a diagnosis of pneumonia, and 143 patients with a diagnosis of pancreatitis. Fluid resuscitation either progressing to subsequent vasopressor administration or not was used as the primary outcome variable to compare general and disease-based modeling. Patients with pancreatitis, pneumonia and the general group all shared three common predictive features as core variables, arterial base excess, lactic acid and platelets. Patients with pneumonia also had non-invasive systolic blood pressure and white blood cells added to the core model, and pancreatitis patients additionally had temperature. Disease-based models had significantly higher values of AUC (p < 0.05) than the general group (0.82 ± 0.02 for pneumonia and 0.83 ± 0.03 for pancreatitis vs. 0.79 ± 0.02 for general patients). Disease-based predictive modeling reveals a different set of predictive variables compared to general modeling and improved performance. Our findings add support to the growing body of evidence advantaging disease specific predictive modeling.
... João ; Juste Ruiz José ; Badenes Casino Margarita ; Thomas Urs P. ; Bell Stuart ; Hertoghs Ma... more ... João ; Juste Ruiz José ; Badenes Casino Margarita ; Thomas Urs P. ; Bell Stuart ; Hertoghs Marleen ; Garabello Roberta ; Seršić Maja ; Wajda Stanislaw ; Turgut Nükhet ; Bogdanović Slavko ; Vinogradov Sergei ; Sorokina Olga ; Hirsch Moshe ; Sobel Lauren ; Rosencranz Armin ...
Communications in Computer and Information Science, 2010
Page 1. Predicting Outcomes of Septic Shock Patients Using Feature Selection Based on Soft Comput... more Page 1. Predicting Outcomes of Septic Shock Patients Using Feature Selection Based on Soft Computing Techniques * André S. Fialho 1,2,3 , Federico Cismondi 1,2,3 , Susana M. Vieira 1,3 , Joao MC Sousa 1,3 , Shane R. Reti ...
IEEE International Conference on Fuzzy Systems, 2013
ABSTRACT We propose the application of probabilistic fuzzy systems (PFS) to model the prediction ... more ABSTRACT We propose the application of probabilistic fuzzy systems (PFS) to model the prediction of early readmission in intensive care unit patients and compare it with the gold-standard method - logistic regression based on the APACHE II score. PFS are characterized by the combination of the linguistic description of the system with the statistical properties of data. On one hand, results point that PFS models perform comparably to the gold-standard method, with AUC values of 0.66±0.03. On the other hand, results also show that PFS models use a significant lower number of variables which, from the clinical practice point of view, suggests improved gains in terms of simplicity.
IEEE International Conference on Fuzzy Systems, 2012
ABSTRACT In the present work, we propose the application of probabilistic fuzzy systems (PFS) to ... more ABSTRACT In the present work, we propose the application of probabilistic fuzzy systems (PFS) to model the prediction of mortality in septic shock patients. This technique is characterized by the combination of the linguistic description of the system with the statistical properties of data. Preliminary results for this particular clinical problem point that PFS models, besides performing as accurately as first order Takagi-Sugeno fuzzy models, also provide probability measures that provide additional clinical information upon which physicians can act on.
2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013
ABSTRACT This paper presents a proposal for a decision support system for the prevention of Inten... more ABSTRACT This paper presents a proposal for a decision support system for the prevention of Intensive Care Unit readmissions, that is being built based on data from the MIMIC II database. The proposed system fuses the results of two distinct classification approaches, one based on available numerical data, and the other based on medical text annotations. The former uses neural fuzzy models and fuzzy modeling in order to provide interpretable classification results. The latter is based on the creation of top-k edited word lists and fuzzy fingerprints. Model fusion is accomplished using a two-tiered fuzzy rule based architecture.
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013
ABSTRACT Shock is a life-threatening medical condition requiring the administration of powerful d... more ABSTRACT Shock is a life-threatening medical condition requiring the administration of powerful drugs - vasopressors. Early identification of these patients is a worthy goal in order to timely prepare them for therapy. A subset composed of the most frequently sampled and readily available variables in an intensive care unit (ICU) was used for clustering patients. Then, a data exploration process was started through the use of fuzzy clustering with the fuzzy cmeans algorithm, where four clusters were obtained and the groups characteristics were analyzed. A relationship between the clusters obtained and the use of vasopressors was found out and these results were visualized with the help of histograms. First, a single model was derived. Then, four models were trained and used for a multi model approach, one for each identified group of patients. In both cases fuzzy models were used as they are universal approximators. For the multi-model approach, two decision criteria were used. First a decision a priori based on the distance from the clusters centers to the patient characteristics was used. Lastly a decision a posteriori approach where each model was used and the final outcome used is based on the uncertainty of the output response to the threshold of each model. The multi model approach with a posteriori decision had a better performance of the two schemes tested, and also performed better than the single general model approach.
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012
ABSTRACT The amount of data generated in the intensive care environment nowadays prohibits the st... more ABSTRACT The amount of data generated in the intensive care environment nowadays prohibits the storage of all the information available. The validation process is time consuming, since nurses have to check every certain periods the data acquired from bedside monitors in order to assess their validity and integrity. This work presents an automatic method for data validation in the intensive care environment, based on an artificial intelligence approach, namely artificial neural networks (ANNs). A real world dataset acquired at Beth Israel Deaconess Medical Center (BIDMC) neonatal intensive care unit (NICU) is used to obtain the validation model and assess its performance. The dataset consists of high frequency sampled data of the level of oxygen saturation (SpO2) of neonates. A subset of 100 neonates was considered for modeling purposes. A total of 7,018,662 samples were available, containing 129,075 validated ones. The performance of the validation model, assessed in terms of its AUC, was of up to 0.75. Both the sensitivity and specificity reached acceptable values according to medical review. Future work would involve a prospective study and validation of the methods proposed in this work.
2012 IEEE Congress on Evolutionary Computation, 2012
ABSTRACT This paper proposes the application of a new binary particle swarm optimization (BPSO) m... more ABSTRACT This paper proposes the application of a new binary particle swarm optimization (BPSO) method to feature selection problems. Two enhanced versions of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm, are proposed. These methods control the swarm variability using the velocity and the similarity between best swarm solutions. The proposed PSO methods use neural networks, fuzzy models and support vector machines in a wrapper approach, and are tested in a benchmark database. It was shown that the proposed BPSO approaches require an inferior simulation time, less selected features and increase accuracy. The best BPSO is then compared with genetic algorithms (GA) and applied to a real medical application, a sepsis patient database. The objective is to predict the outcome (survived or deceased) of the sepsis patients. It was shown that the proposed BPSO approaches are similar in terms of model accuracy when compared to GA, while requiring an inferior simulation time and less selected features.
2006 IEEE International Conference on Fuzzy Systems, 2006
... results The fault accommodation is made considering the three outputs of container gantry cra... more ... results The fault accommodation is made considering the three outputs of container gantry crane: horizontal position ... Table II shows the control results when the fault F1 occurs, using algorithm for ... sum squared error between the references and the outputs of the system after the ...
To compare general and disease-based modeling for fluid resuscitation and vasopressor use in inte... more To compare general and disease-based modeling for fluid resuscitation and vasopressor use in intensive care units. Retrospective cohort study involving 2944 adult medical and surgical intensive care unit (ICU) patients receiving fluid resuscitation. Within this cohort there were two disease-based groups, 802 patients with a diagnosis of pneumonia, and 143 patients with a diagnosis of pancreatitis. Fluid resuscitation either progressing to subsequent vasopressor administration or not was used as the primary outcome variable to compare general and disease-based modeling. Patients with pancreatitis, pneumonia and the general group all shared three common predictive features as core variables, arterial base excess, lactic acid and platelets. Patients with pneumonia also had non-invasive systolic blood pressure and white blood cells added to the core model, and pancreatitis patients additionally had temperature. Disease-based models had significantly higher values of AUC (p < 0.05) than the general group (0.82 ± 0.02 for pneumonia and 0.83 ± 0.03 for pancreatitis vs. 0.79 ± 0.02 for general patients). Disease-based predictive modeling reveals a different set of predictive variables compared to general modeling and improved performance. Our findings add support to the growing body of evidence advantaging disease specific predictive modeling.
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Papers by Susana Vieira