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Umberto Picchini
Researcher at Chalmers University of Technology
Publications - 41
Citations - 1035
Umberto Picchini is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Inference & Stochastic differential equation. The author has an hindex of 14, co-authored 36 publications receiving 932 citations. Previous affiliations of Umberto Picchini include National Research Council & University of Copenhagen.
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Journal ArticleDOI
Effects of levosimendan on right ventricular afterload in patients with acute respiratory distress syndrome: a pilot study.
Andrea Morelli,Jean-Louis Teboul,Salvatore Maurizio Maggiore,Antoine Vieillard-Baron,Monica Rocco,Giorgio Conti,Andrea De Gaetano,Umberto Picchini,Alessandra Orecchioni,Iacopo Carbone,Luigi Tritapepe,Paolo Pietropaoli,Martin Westphal +12 more
TL;DR: Evidence is provided that levosimendan improves right ventricular performance through pulmonary vasodilator effects in septic patients with ARDS and is able to improve the overall prognosis of patients with sepsis and ARDS.
Journal ArticleDOI
Prophylactic fenoldopam for renal protection in sepsis: a randomized, double blind, placebo-controlled pilot trial
Andrea Morelli,Zaccaria Ricci,Rinaldo Bellomo,Claudio Ronco,Monica Rocco,Giorgio Conti,Andrea De Gaetano,Umberto Picchini,Alessandra Orecchioni,Monica Portieri,Flaminia Coluzzi,Patrizia Porzi,Paola Serio,Annunziata Bruno,Paolo Pietropaoli +14 more
TL;DR: Compared with placebo, low-dose fenoldopam resulted in a smaller increase in serum creatinine in septic patients, and the clinical significance of this finding is uncertain.
Journal ArticleDOI
Stochastic Differential Mixed-Effects Models
TL;DR: In this paper, the authors proposed a computationally fast approximated maximum likelihood procedure for the estimation of the non-random parameters and the random effects in stochastic differential mixed-effects models.
Journal ArticleDOI
Practical estimation of high dimensional stochastic differential mixed-effects models
TL;DR: A parameter estimation method is proposed and computational guidelines for an efficient implementation are given, and the method is evaluated using simulations from standard models like the two-dimensional Ornstein-Uhlenbeck (OU) and the square root models.
Journal ArticleDOI
Inference for SDE Models via Approximate Bayesian Computation
TL;DR: This work considers simulation studies for a pharmacokinetics/pharmacodynamics model and for stochastic chemical reactions and provides a Matlab package that implements the proposed ABC-MCMC algorithm.