NAPROUS, Alexandre; AVALOS FERNANDEZ, Marta; PRADEAU, Catherine; LAGARDE, Emmanuel; GIL-JARDINE, Cedric

doi:10.32473/flairs.v35i.130712

NAPROUS, Alexandre
Bordeaux population health [BPH]

AVALOS FERNANDEZ, Marta

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

PRADEAU, Catherine
Bordeaux population health [BPH]

Langue

Communication dans un congrès avec actes

Ce document a été publié dans

The International FLAIRS Conference Proceedings, FLAIRS-35 - 35th International Florida Artificial Intelligence Research Society Conference, 2022-05-15, Hutchinson. 2022-05-04, vol. 35

Résumé en anglais

By focusing on symptoms and not diagnoses, the socalled syndromic surveillance system gains in immediacy what it loses in specificity with respect to other more traditional options for public health surveillance. Reports of calls to emergency medical communication centers (EMCC) supplemented by the data collected by the rescue workers who arrived on the scene constitute a cost-effective and rich source of information. Unfortunately, EMCC data are infrequently used and their utility has not been demonstrated. The aim of this study was to explore the usefulness for public health surveillance of EMCC data when analyzed using text mining and visualization tools. Transformer-based deep learning architectures were used to classify call reports according to the reason for the call. We also extracted indicators that could serve as proxy measures using a keyword-search algorithm. We then developed a dashboard visualization tool to enable dynamic and spatial exploratory analyses. Finally, we explored the potential of this tool for two applications. While the tool proved unable to detect Covid-19 outbreaks, it appeared to be promising for a better understanding of territorial inequalities in healthcare access.< Réduire

Mots clés en anglais

Syndromic surveillance

URI

https://oskar-bordeaux.fr/handle/20.500.12278/172169

DOI

http://dx.doi.org/10.32473/flairs.v35i.130712

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219

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Public Health surveillance from emergency call center data: visualization dashboard and NLP of call reports: Public Health surveillance from EMCC data

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Mots clés en anglais

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