Výsledky vyhledávání - "Stefano PELUSO"

Akademický článek

Spatio-temporal distribution, prediction and relationship of three major acute cardiovascular events: Out-of-hospital cardiac arrest, ST-elevation myocardial infarction and stroke

Autor: Angelo Auricchio, Tommaso Scquizzato, Federico Ravenda, Ruggero Cresta, Stefano Peluso, Maria Luce Caputo, Stefano Tonazzi, Claudio Benvenuti, Antonietta Mira

Publikováno v: Resuscitation Plus, Vol 20, Iss , Pp 100810- (2024)

Background: Predicting the incidence of time-sensitive cardiovascular diseases like out-of-hospital cardiac arrest (OHCA), ST-elevation myocardial infarction (STEMI), and stroke can reduce time to treatment and improve outcomes. This study analysed t

Externí odkaz: https://doaj.org/article/4c71112187d543888d6e605804f25ced

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Akademický článek

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Akademický článek

Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project

Autor: Daniele Roberto Giacobbe, Cristina Marelli, Sara Mora, Sabrina Guastavino, Chiara Russo, Giorgia Brucci, Alessandro Limongelli, Antonio Vena, Malgorzata Mikulska, Maryam Tayefi, Stefano Peluso, Alessio Signori, Antonio Di Biagio, Anna Marchese, Cristina Campi, Mauro Giacomini, Matteo Bassetti

Publikováno v: Annals of Medicine, Vol 55, Iss 2 (2023)

AbstractBackground Candidemia is associated with a heavy burden of morbidity and mortality in hospitalized patients. The availability of blood culture results could require up to 48–72 h after blood draw; thus, early treatment decisions are made in

Externí odkaz: https://doaj.org/article/2f1948f9a47c4f839d95b8438f5761f9

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Akademický článek

Spatio-temporal prediction model of out-of-hospital cardiac arrest: Designation of medical priorities and estimation of human resources requirement.

Autor: Angelo Auricchio, Stefano Peluso, Maria Luce Caputo, Jost Reinhold, Claudio Benvenuti, Roman Burkart, Roberto Cianella, Catherine Klersy, Enrico Baldi, Antonietta Mira

Publikováno v: PLoS ONE, Vol 15, Iss 8, p e0238067 (2020)

AimsTo determine the out-of-hospital cardiac arrest (OHCA) rates and occurrences at municipality level through a novel statistical model accounting for temporal and spatial heterogeneity, space-time interactions and demographic features. We also aime

Externí odkaz: https://doaj.org/article/142f3d0c48d14c6685cf944900ef5d71

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Akademický článek

Evaluating health facility access using Bayesian spatial models and location analysis methods.

Autor: Nicholas J Tierney, Antonietta Mira, H Jost Reinhold, Giuseppe Arbia, Samuel Clifford, Angelo Auricchio, Tiziano Moccetti, Stefano Peluso, Kerrie L Mengersen

Publikováno v: PLoS ONE, Vol 14, Iss 8, p e0218310 (2019)

BackgroundFloating catchment methods have recently been applied to identify priority regions for Automated External Defibrillator (AED) deployment, to aid in improving Out of Hospital Cardiac Arrest (OHCA) survival. This approach models access as a s

Externí odkaz: https://doaj.org/article/002ba953acdc46a19ccd74a9e0673403

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Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies

Autor: Francesco Denti, Stefano Peluso, Michele Guindani, Antonietta Mira

The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two com

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6564bdeef94198c80fb4aa5ff636da29
https://hdl.handle.net/10281/413537

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Gold and bubbles: an impossible binomial? A review of historical and current evidence

Autor: Edoardo Beretta, Stefano Peluso

Gold has a far-reaching history of ‘safe haven’ or ‘anchor of stability’. However, the epitome of precious metals has recently deviated from its historical price trend. Fluctuations and volatility have increased too. We argue that gold is not

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af33095a0420b311426cab24af354856
http://hdl.handle.net/10281/317055

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Network Structure Learning Under Uncertain Interventions

Autor: Federico Castelletti, Stefano Peluso

Gaussian Directed Acyclic Graphs (DAGs) represent a powerful tool for learning the network of dependencies among variables, a task which is of primary interest in many fields and specifically in biology. Different DAGs may encode equivalent condition

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a0c52419932d5f47f4a8dd31cd60047

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