Public Health surveillance from emergency call center data: visualization dashboard and NLP of call reports
| Autor: | Naprous, Alexandre, Avalos, Marta, Pradeau, Catherine, Lagarde, Emmanuel, Gil-Jardine, Cédric |
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| Přispěvatelé: | Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Bordeaux (UB), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU de Bordeaux Pellegrin [Bordeaux], Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, This work was supported by the French National Research Agency (ANR) under the grant COSAM 'Epidemiological surveillance of the COVID-19 pandemic period by real-time automatic classification of clinical notes from 15 emergency call centers using Transformer-based artificial neural networks' (project number ANR-20-COVl-0004-01). The authors’ research teams had annual grants from the University of Bordeaux, INSERM U1219 and INRIA., Avalos, Marta |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
[STAT.AP]Statistics [stat]/Applications [stat.AP]
[STAT.ME] Statistics [stat]/Methodology [stat.ME] [STAT.AP] Statistics [stat]/Applications [stat.AP] [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] [STAT.CO]Statistics [stat]/Computation [stat.CO] [STAT.CO] Statistics [stat]/Computation [stat.CO] [STAT.ME]Statistics [stat]/Methodology [stat.ME] [STAT.ML] Statistics [stat]/Machine Learning [stat.ML] |
| Zdroj: | FLAIRS-35-35th International Florida Artificial Intelligence Research Society Conference FLAIRS-35-35th International Florida Artificial Intelligence Research Society Conference, May 2022, Hutchinson, Florida, United States. ⟨10.32473/flairs.v35i.130712⟩ |
| DOI: | 10.32473/flairs.v35i.130712⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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