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Master Thesis

Syndromes grippaux au SAMU 33 : application d’un modèle d’intelligence artificielle sur les dossiers de régulation de 2010 à 2020 et variation de l’activité chez les personnes âgées

Abstract : The need for reliable public health indicators is crucial for appropriate resource responses, particularly in the current context of the COVID-19 pandemic. In France, medical regulation offers a significant amount of data on the calls made. Thanks to "Natural language processing", we have obtained data from 2010 to 2020 from SMUR 33 regulation concerning the flu symptoms of all patients, i.e. more than 2 million free texts, categorized according to age. We took patients over 75 years of age, but also the category 65-75 years of age. The results show an overall increase in the number of calls, all causes combined and for flu-like symptoms. With different age categories having different increases: the over 65s having the highest rate of increase in the total number of calls and for flu syndrome. Moreover, the year 2020 with the emergence of COVID 19 finds an extreme increase in the number of calls for flu syndromes over a short period of time with, however, a lower proportion of calls made by those over 75 years of age. The use of artificial intelligence with natural language processing of the contents of medical regulation files enables effective epidemiological surveillance and, in exceptional health situations, provides an additional efficient and accessible tool.
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https://dumas.ccsd.cnrs.fr/dumas-03085866
Contributor : Médecine Ub Connect in order to contact the contributor
Submitted on : Tuesday, December 22, 2020 - 9:30:01 AM
Last modification on : Wednesday, January 27, 2021 - 3:12:40 AM

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Med_spe_2020_Calbete.pdf
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  • HAL Id : dumas-03085866, version 1

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Maialen Calbete. Syndromes grippaux au SAMU 33 : application d’un modèle d’intelligence artificielle sur les dossiers de régulation de 2010 à 2020 et variation de l’activité chez les personnes âgées. Sciences du Vivant [q-bio]. 2020. ⟨dumas-03085866⟩

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