Tags

Type your tag names separated by a space and hit enter

Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification.
Scand J Trauma Resusc Emerg Med. 2021 Mar 31; 29(1):55.SJ

Abstract

OBJECTIVES

During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdown. We assessed whether the automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators.

METHODS

We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 39,907 manually-coded free-text reports.

RESULTS

The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain and stress and anxiety, peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. No noticeable trends in relation to lockdown was found for other groups of reasons including gastroenteritis and abdominal pain, stroke, suicide and self-harm, pregnancy and delivery problems.

DISCUSSION

The first wave of the COVID-19 crisis came along with increased levels of stress and anxiety but no increase in alcohol intoxication and violence. As expected, call related to road traffic crashes sharply decreased. The sharp decrease in the number of calls for malaise was more surprising.

CONCLUSION

The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. The COVID-19 crisis and/or lockdown induced deep modifications in the population health profile.

Authors+Show Affiliations

Inserm, ISPED, University of Bordeaux, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation team, Bordeaux, France. University Hospital of Bordeaux, Pole of Emergency Medicine, Bordeaux, France.Inserm, ISPED, University of Bordeaux, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation team, Bordeaux, France.Inserm, ISPED, University of Bordeaux, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation team, Bordeaux, France. University Hospital of Bordeaux, Pole of Emergency Medicine, Bordeaux, France.University Hospital of Bordeaux, Pole of Emergency Medicine, Bordeaux, France.Inserm, ISPED, University of Bordeaux, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation team, Bordeaux, France. University Hospital of Bordeaux, Pole of Emergency Medicine, Bordeaux, France.Inserm, ISPED, University of Bordeaux, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation team, Bordeaux, France. University Hospital of Bordeaux, Pole of Emergency Medicine, Bordeaux, France.Inserm, ISPED, University of Bordeaux, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation team, Bordeaux, France. University Hospital of Bordeaux, Pole of Emergency Medicine, Bordeaux, France.Inserm, ISPED, University of Bordeaux, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation team, Bordeaux, France. University Hospital of Bordeaux, Pole of Emergency Medicine, Bordeaux, France.Inserm, ISPED, University of Bordeaux, Bordeaux Population Health Research Center Inserm U1219 Injury Epidemiology Transport Occupation team, Bordeaux, France. Emmauel.lagarde@u-bordeaux.fr.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

33789721

Citation

Gil-Jardiné, Cédric, et al. "Trends in Reasons for Emergency Calls During the COVID-19 Crisis in the Department of Gironde, France Using Artificial Neural Network for Natural Language Classification." Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, vol. 29, no. 1, 2021, p. 55.
Gil-Jardiné C, Chenais G, Pradeau C, et al. Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification. Scand J Trauma Resusc Emerg Med. 2021;29(1):55.
Gil-Jardiné, C., Chenais, G., Pradeau, C., Tentillier, E., Revel, P., Combes, X., Galinski, M., Tellier, E., & Lagarde, E. (2021). Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 29(1), 55. https://doi.org/10.1186/s13049-021-00862-w
Gil-Jardiné C, et al. Trends in Reasons for Emergency Calls During the COVID-19 Crisis in the Department of Gironde, France Using Artificial Neural Network for Natural Language Classification. Scand J Trauma Resusc Emerg Med. 2021 Mar 31;29(1):55. PubMed PMID: 33789721.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification. AU - Gil-Jardiné,Cédric, AU - Chenais,Gabrielle, AU - Pradeau,Catherine, AU - Tentillier,Eric, AU - Revel,Philippe, AU - Combes,Xavier, AU - Galinski,Michel, AU - Tellier,Eric, AU - Lagarde,Emmanuel, Y1 - 2021/03/31/ PY - 2020/11/09/received PY - 2021/03/04/accepted PY - 2021/4/1/entrez PY - 2021/4/2/pubmed PY - 2021/4/15/medline KW - COVID-19 KW - Emergency medical communication centers KW - Lockdown KW - Public health SP - 55 EP - 55 JF - Scandinavian journal of trauma, resuscitation and emergency medicine JO - Scand J Trauma Resusc Emerg Med VL - 29 IS - 1 N2 - OBJECTIVES: During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdown. We assessed whether the automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators. METHODS: We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 39,907 manually-coded free-text reports. RESULTS: The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain and stress and anxiety, peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. No noticeable trends in relation to lockdown was found for other groups of reasons including gastroenteritis and abdominal pain, stroke, suicide and self-harm, pregnancy and delivery problems. DISCUSSION: The first wave of the COVID-19 crisis came along with increased levels of stress and anxiety but no increase in alcohol intoxication and violence. As expected, call related to road traffic crashes sharply decreased. The sharp decrease in the number of calls for malaise was more surprising. CONCLUSION: The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. The COVID-19 crisis and/or lockdown induced deep modifications in the population health profile. SN - 1757-7241 UR - https://www.unboundmedicine.com/medline/citation/33789721/Trends_in_reasons_for_emergency_calls_during_the_COVID_19_crisis_in_the_department_of_Gironde_France_using_artificial_neural_network_for_natural_language_classification_ L2 - https://sjtrem.biomedcentral.com/articles/10.1186/s13049-021-00862-w DB - PRIME DP - Unbound Medicine ER -