C
Christine Choirat
Researcher at ETH Zurich
Publications - 76
Citations - 3227
Christine Choirat is an academic researcher from ETH Zurich. The author has contributed to research in topics: Mathematical psychology & Causal inference. The author has an hindex of 18, co-authored 73 publications receiving 2183 citations. Previous affiliations of Christine Choirat include University of Insubria & École Polytechnique Fédérale de Lausanne.
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Journal ArticleDOI
Air Pollution and Mortality in the Medicare Population.
Qian Di,Yan Wang,Antonella Zanobetti,Yun Wang,Petros Koutrakis,Christine Choirat,Francesca Dominici,Joel Schwartz +7 more
TL;DR: In the entire Medicare population, there was significant evidence of adverse effects related to exposure to PM2.5 and ozone at concentrations below current national standards.
Journal ArticleDOI
Association of Short-term Exposure to Air Pollution With Mortality in Older Adults
Qian Di,Lingzhen Dai,Yun Wang,Antonella Zanobetti,Christine Choirat,Joel Schwartz,Francesca Dominici +6 more
TL;DR: In the US Medicare population from 2000 to 2012, short-term exposures to PM2.5 and warm-season ozone were significantly associated with increased risk of mortality, suggesting that these standards may need to be reevaluated.
Journal ArticleDOI
An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution
Qian Di,Qian Di,Heresh Amini,Liuhua Shi,Itai Kloog,Rachel F. Silvern,James T. Kelly,M. Benjamin Sabath,Christine Choirat,Petros Koutrakis,Alexei Lyapustin,Yujie Wang,Loretta J. Mickley,Joel Schwartz +13 more
TL;DR: An ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1’km × 1 km across the contiguous United States allows epidemiologists to accurately estimate the adverse health effect of PM 2.5.
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Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging.
Qian Di,Qian Di,Heresh Amini,Liuhua Shi,Liuhua Shi,Itai Kloog,Rachel F. Silvern,James T. Kelly,M. Benjamin Sabath,Christine Choirat,Petros Koutrakis,Alexei Lyapustin,Yujie Wang,Loretta J. Mickley,Joel Schwartz +14 more
TL;DR: An ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models is proposed to assess NO2 level with high accuracy.
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Readmission Rates After Passage of the Hospital Readmissions Reduction Program: A Pre-Post Analysis.
TL;DR: The Medicare Hospital Readmissions Reduction Program (HRRP) introduced the prospect of financially penalizing hospitals based on their previous performance, and time-series data on readmission rates for Medicare fee-for-service enrollees from 2000 to 2013 were analyzed to answer 2 questions.