Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018

Autor: Caroline Landelle, Ursula von Schenck, Jean-Luc Bosson, Christophe Cancé, Svetlana Artemova, Rui Fa, Daniel Stoessel, Hadiseh Nowparast Rostami, Pierre-Ephrem Madiot, Jean-Marie Januel, Daniel Pagonis, Meghann Gallouche, Frederic Olive, Alexandre Moreau-Gaudry, Sigurd Prieur

Publikováno v: BMJ Open, Vol 13, Iss 8 (2023)

Purpose In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the i

Externí odkaz: https://doaj.org/article/07123d31bb3f4e519b801559ae203acb

COVID-19 Geographical Maps and Clinical Data Warehouse PREDIMED

Autor: Svetlana, Artemova, Alban, Caporossi, Christophe, Cancé, Pierre-Ephrem, Madiot, Benjamin, Nemoz, Sylvie, Larrat, Alexandre, Moreau-Gaudry, Jean-Luc, Bosson

Publikováno v: Studies in health technology and informatics. 290

PREDIMED, Clinical Data Warehouse of Grenoble Alps University Hospital, is currently participating in daily COVID-19 epidemic follow-up via spatial and chronological analysis of geographical maps. This monitoring is aimed for cluster detection and vu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::7228ec2b8f5159b706d73ec1046f90a3
https://pubmed.ncbi.nlm.nih.gov/35673198

Medico-administrative data combined with agricultural practices data to retrospectively estimate pesticide use by agricultural workers

Autor: Anne Maitre, Charlotte Maugard, Pauline Achard, Delphine Bosson-Rieutort, Vincent Bonneterre, Christophe Cancé, Damien Ozenfant, Johan Spinosi

Publikováno v: Journal of Exposure Science and Environmental Epidemiology
Journal of Exposure Science and Environmental Epidemiology, Nature Publishing Group, 2019, ⟨10.1038/s41370-019-0166-x⟩

This work is part of a global project aiming to use medico-administrative big data from the whole French agricultural population (~3 millions), collected through their mandatory health insurance system (Mutualite Sociale Agricole), to highlight assoc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4e5b6e72ee08626ef837b2f57f2b89d
https://doi.org/10.1038/s41370-019-0166-x

0431 How can we avoid re-identification risk in big-data analysis? proposition of a new strategy of geographical subdivisions using gis tools

Autor: Delphine Bosson-Rieutort, Christophe Cancé, Vincent Bonneterre, Charlotte Maugard, Pauline Achard, Olivier François

Publikováno v: Eliminating Occupational Disease: Translating Research into Action, EPICOH 2017, EPICOH 2017, 28–31 August 2017, Edinburgh, UK
Eliminating Occupational Disease: Translating Research into Action, EPICOH 2017, EPICOH 2017, 28–31 August 2017, Edinburgh, UK, 2017, Edinburgh, United Kingdom. pp.A136.2-A137

In order to look for relevant signals for detection of emerging occupational diseases among agricultural workers, we developed a data-mining approach applied on health-insurance data (see C. Maugard communication). Applied on the databases of the Fre

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57dfa687a6ca5cbec318b31c638733eb
https://hal.archives-ouvertes.fr/hal-01996501