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Institution

University of Milano-Bicocca

EducationMilan, Italy
About: University of Milano-Bicocca is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Population & Blood pressure. The organization has 8972 authors who have published 22322 publications receiving 620484 citations. The organization is also known as: Università degli Studi di Milano-Bicocca & Universita degli Studi di Milano-Bicocca.


Papers
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Journal ArticleDOI
08 Aug 2017-JAMA
TL;DR: In this Viewpoint, the potential unintended consequences that may result from the application of ML-DSS in clinical practice are considered.
Abstract: Over the past decade, machine learning techniques have made substantial advances in many domains. In health care, global interest in the potential of machine learning has increased; for example, a deep learning algorithm has shown high accuracy in detecting diabetic retinopathy.1 There have been suggestions that machine learning will drive changes in health care within a few years, specifically in medical disciplines that require more accurate prognostic models (eg, oncology) and those based on pattern recognition (eg, radiology and pathology). However, comparative studies on the effectiveness of machine learning–based decision support systems (ML-DSS) in medicine are lacking, especially regarding the effects on health outcomes. Moreover, the introduction of new technologies in health care has not always been straightforward or without unintended and adverse effects.2 In this Viewpoint we consider the potential unintended consequences that may result from the application of ML-DSS in clinical practice.

570 citations

Journal ArticleDOI
TL;DR: The impact of the three main sources of potential bias in the traditional approach to mediation analyses are reviewed and discussed: (i) mediator-outcome confounding; (ii) exposure-mediator interaction and (iii) mediATOR- outcome confounding affected by the exposure.
Abstract: In epidemiological studies it is often necessary to disentangle the pathways that link an exposure to an outcome. Typically the aim is to identify the total effect of the exposure on the outcome, the effect of the exposure that acts through a given set of mediators of interest (indirect effect) and the effect of the exposure unexplained by those same mediators (direct effect). The traditional approach to mediation analysis is based on adjusting for the mediator in standard regression models to estimate the direct effect. However, several methodological papers have shown that under a number of circumstances this traditional approach may produce flawed conclusions. Through a better understanding of the causal structure of the variables involved in the analysis, with a formal definition of direct and indirect effects in a counterfactual framework, alternative analytical methods have been introduced to improve the validity and interpretation of mediation analysis. In this paper, we review and discuss the impact of the three main sources of potential bias in the traditional approach to mediation analyses: (i) mediator-outcome confounding;(ii) exposure-mediator interaction and (iii) mediator-outcome confounding affected by the exposure. We provide examples and discuss the impact these sources have in terms of bias.

564 citations

Journal ArticleDOI
S. Chatrchyan, Khachatryan1, Albert M. Sirunyan, Armen Tumasyan  +2384 moreInstitutions (207)
26 May 2014
TL;DR: In this paper, a description of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices is provided.
Abstract: A description is provided of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices. Despite the very hostile environment at the LHC, the performance obtained with these algorithms is found to be excellent. For tt events under typical 2011 pileup conditions, the average track-reconstruction efficiency for promptly-produced charged particles with transverse momenta of p_T > 0.9GeV is 94% for pseudorapidities of |η| < 0.9 and 85% for 0.9 < |η| < 2.5. The inefficiency is caused mainly by hadrons that undergo nuclear interactions in the tracker material. For isolated muons, the corresponding efficiencies are essentially 100%. For isolated muons of p_T = 100GeV emitted at |η| < 1.4, the resolutions are approximately 2.8% in p_T, and respectively, 10μm and 30μm in the transverse and longitudinal impact parameters. The position resolution achieved for reconstructed primary vertices that correspond to interesting pp collisions is 10–12μm in each of the three spatial dimensions. The tracking and vertexing software is fast and flexible, and easily adaptable to other functions, such as fast tracking for the trigger, or dedicated tracking for electrons that takes into account bremsstrahlung.

559 citations

Journal ArticleDOI
TL;DR: The newly developed EQ-5D-Y is a useful tool to measure HRQOL in young people in an age-appropriate manner and was satisfactorily understood by children and adolescents in different countries.
Abstract: Purpose To develop a self-report version of the EQ-5D for younger respondents, named the EQ-5D-Y (Youth); to test its comprehensibility for children and adolescents and to compare results obtained using the standard adult EQ-5D and the EQ-5D-Y.

556 citations


Authors

Showing all 9226 results

NameH-indexPapersCitations
Carlo Rovelli1461502103550
Giuseppe Mancia1451369139692
Marco Bersanelli142526105135
Teruki Kamon1422034115633
Marco Colonna13951271166
M. I. Martínez134125179885
A. Mennella13246393236
Roberto Salerno132119783409
Federico Ferri132137689337
Marco Paganoni132143888482
Arabella Martelli131131884029
Sandra Malvezzi129132684401
Andrea Massironi129111578457
Marco Pieri129128582914
Cristina Riccardi129162791452
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023173
2022349
20212,468
20202,253
20191,906
20181,706