S
Simão Paredes
Researcher at University of Coimbra
Publications - 69
Citations - 343
Simão Paredes is an academic researcher from University of Coimbra. The author has contributed to research in topics: Risk assessment & Risk management tools. The author has an hindex of 10, co-authored 66 publications receiving 278 citations. Previous affiliations of Simão Paredes include Polytechnic Institute of Coimbra & Instituto Superior de Engenharia de Coimbra.
Papers
More filters
Journal ArticleDOI
Prediction of acute hypotensive episodes by means of neural network multi-models
TL;DR: The effectiveness of the methodology was validated in the context of the 10th PhysioNet/Computers in Cardiology Challenge-Predicting Acute Hypotensive Episodes, applied to a specific set of blood pressure signals, available in MIMIC-II database.
Journal ArticleDOI
Prediction of Heart Failure Decompensation Events by Trend Analysis of Telemonitoring Data
Jorge Henriques,Paulo Carvalho,Simão Paredes,Teresa Rocha,Joerg Habetha,Manuel J. Antunes,João Morais +6 more
TL;DR: The obtained results suggest that the physiological data have predictive value, and in particular, that the proposed scheme is particularly appropriate to address the early detection of HF decompensation.
Journal ArticleDOI
Comparison of different methods of measuring similarity in physiologic time series
A. Kianimajd,Maria da Graça Ruano,Paulo Carvalho,Jorge Henriques,Teresa Rocha,Simão Paredes,António E. Ruano +6 more
TL;DR: Results demonstrate that the time domain Correlation Coefficient is the most robust method while the Discrete Wavelet Transform is the elected one between the transform-based methods tested.
Journal ArticleDOI
Long term cardiovascular risk models' combination
TL;DR: This work addresses two major drawbacks of the current cardiovascular risk score systems: reduced number of risk factors considered by each individual tool and the inability of these tools to deal with incomplete information.
Proceedings ArticleDOI
Wavelet based time series forecast with application to acute hypotensive episodes prediction
TL;DR: The particular problem of forecasting acute hypotensive episodes (AHE) occurring in intensive care units was used to prove the effectiveness of the proposed strategy, which combines the flexibility and learning abilities of neural networks with a compact description of the signals, inherent to wavelets.