En poursuivant votre navigation sur ce site, vous acceptez l'utilisation d'un simple cookie d'identification. Aucune autre exploitation n'est faite de ce cookie. OK
1

Big data for health: a Bayesian spatio-temporal analysis for predicting cardiac risk in Ticino and optimal defibrillators positioning

Sélection Signaler une erreur
Multi angle
Auteurs : Mira, Antonietta (Auteur de la Conférence)
CIRM (Editeur )

Loading the player...

Résumé : The term ‘Public Access Defibrillation' (PAD) is referred to programs based on the placement of Automated External Defibrillators (AED) in key locations along cities' territory together with the development of a training plan for users (first responders). PAD programs are considered necessary since time for intervention in cases of sudden cardiac arrest outside of a medical environment (out-of-hospital cardiocirculatory arrest, OHCA) is strongly limited: survival potential decreases from a 67% baseline by 7 to 10% for each minute of delay in first defibrillation. However, it is widely recognized that current PAD performance is largely below its full potential. We provide a Bayesian spatio-temporal statistical model for predidicting OHCAs. Then we construct a risk map for Ticino, adjusted for demographic covariates, that explains and forecasts the spatial distribution of OHCAs, their temporal dynamics, and how the spatial distribution changes over time. The objective is twofold: to efficiently estimate, in each area of interest, the occurrence intensity of the OHCA event and to suggest a new optimized distribution of AEDs that accounts for population exposure to the geographic risk of OHCA occurrence and that includes both displacement of current devices and installation of new ones.

Codes MSC :
62F15 - Bayesian inference
62H11 - Directional data; spatial statistics
62P10 - Applications of statistics to biology and medical sciences
91B30 - Risk theory, insurance

    Informations sur la Vidéo

    Réalisateur : Hennenfent, Guillaume
    Langue : Anglais
    Date de publication : 06/12/2018
    Date de captation : 26/11/2018
    Sous collection : Research talks
    arXiv category : Computation ; Statistics Theory ; Methodology
    Domaine : Probability & Statistics
    Format : MP4 (.mp4) - HD
    Durée : 00:33:03
    Audience : Researchers
    Download : https://videos.cirm-math.fr/2018-11-26_Mira.mp4

Informations sur la Rencontre

Nom de la rencontre : Jean-Morlet chair - Workshop: Young Bayesians and big data for social good / Chaire Jean-Morlet - Workshop : Jeunes Bayésiens et big data pour le bien social
Organisateurs de la rencontre : Mengersen, Kerrie ; Pommeret, Denys ; Pudlo, Pierre ; Robert, Christian P.
Dates : 23/11/2018 - 26/11/2018
Année de la rencontre : 2018
URL Congrès : https://www.chairejeanmorlet.com/1911.html

Données de citation

DOI : 10.24350/CIRM.V.19479003
Citer cette vidéo: Mira, Antonietta (2018). Big data for health: a Bayesian spatio-temporal analysis for predicting cardiac risk in Ticino and optimal defibrillators positioning. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19479003
URI : http://dx.doi.org/10.24350/CIRM.V.19479003

Voir aussi

Bibliographie



Sélection Signaler une erreur