Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality.
Journal
Npj mental health research
ISSN: 2731-4251
Titre abrégé: Npj Ment Health Res
Pays: England
ID NLM: 9918592488906676
Informations de publication
Date de publication:
14 Feb 2024
14 Feb 2024
Historique:
received:
01
02
2023
accepted:
06
12
2023
medline:
13
4
2024
pubmed:
13
4
2024
entrez:
12
4
2024
Statut:
epublish
Résumé
There is an urgent need to monitor the mental health of large populations, especially during crises such as the COVID-19 pandemic, to timely identify the most at-risk subgroups and to design targeted prevention campaigns. We therefore developed and validated surveillance indicators related to suicidality: the monthly number of hospitalisations caused by suicide attempts and the prevalence among them of five known risks factors. They were automatically computed analysing the electronic health records of fifteen university hospitals of the Paris area, France, using natural language processing algorithms based on artificial intelligence. We evaluated the relevance of these indicators conducting a retrospective cohort study. Considering 2,911,920 records contained in a common data warehouse, we tested for changes after the pandemic outbreak in the slope of the monthly number of suicide attempts by conducting an interrupted time-series analysis. We segmented the assessment time in two sub-periods: before (August 1, 2017, to February 29, 2020) and during (March 1, 2020, to June 31, 2022) the COVID-19 pandemic. We detected 14,023 hospitalisations caused by suicide attempts. Their monthly number accelerated after the COVID-19 outbreak with an estimated trend variation reaching 3.7 (95%CI 2.1-5.3), mainly driven by an increase among girls aged 8-17 (trend variation 1.8, 95%CI 1.2-2.5). After the pandemic outbreak, acts of domestic, physical and sexual violence were more often reported (prevalence ratios: 1.3, 95%CI 1.16-1.48; 1.3, 95%CI 1.10-1.64 and 1.7, 95%CI 1.48-1.98), fewer patients died (p = 0.007) and stays were shorter (p < 0.001). Our study demonstrates that textual clinical data collected in multiple hospitals can be jointly analysed to compute timely indicators describing mental health conditions of populations. Our findings also highlight the need to better take into account the violence imposed on women, especially at early ages and in the aftermath of the COVID-19 pandemic.
Identifiants
pubmed: 38609541
doi: 10.1038/s44184-023-00046-7
pii: 10.1038/s44184-023-00046-7
doi:
Types de publication
Journal Article
Langues
eng
Pagination
6Informations de copyright
© 2024. The Author(s).
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