Federico Cismondi

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11 Current models for predicting intensive care unit (ICU) readmission have moderate predictive value, and can utilize up to twelve variables that may be assessed at various points of the ICU inpatient stay. We postulate that greater predictive value can be achieved with fewer physiological variables, some of which can be assessed in the 24 hours before(More)
BACKGROUND The multiplicity of information sources for data acquisition in modern intensive care units (ICUs) makes the resulting databases particularly susceptible to missing data. Missing data can significantly affect the performance of predictive risk modeling, an important technique for developing medical guidelines. The two most commonly used(More)
One consequence of the increasing amount of data stored during acquisition processes is that sampled time series are more prone to be collected in a misaligned uneven fashion and/or be partly lost or unavailable (missing data). Due to their severe impact on data mining techniques, this work proposes methods to (a) align misaligned unevenly sampled data, (b)(More)
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(More)
Vasopressors belong to a powerful class of drugs used in the management of systemic shock in ill patients. The administration of a vasopressor involves the non-trivial process of inserting a central venous catheter. This procedure carries with it inherent risks which are increased when done under urgency such as in the case of unexpected systemic shock. The(More)
INTRODUCTION Whether red blood cell (RBC) transfusion is beneficial remains controversial. In both retrospective and prospective evaluations, transfusion has been associated with adverse, neutral, or protective effects. These varying results likely stem from a complex interplay between transfusion, patient characteristics, and clinical context. The(More)
Real-world databases often contain missing data and existing correction algorithms deliver varying performance. Also, most modeling techniques are not suitable to deal with them automatically. In this study we examine different approaches to predicting septic shock in the presence of missing data. Some preprocessing techniques for managing missing data(More)
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(More)