Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality - Institut Mondor de Recherche Biomédicale
Article Dans Une Revue npj Mental Health Research Année : 2024

Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality

Romain Bey
  • Fonction : Auteur
Ariel Cohen
  • Fonction : Auteur
Vincent Trebossen
  • Fonction : Auteur
Basile Dura
  • Fonction : Auteur
Pierre-Alexis Geoffroy
  • Fonction : Auteur
Benjamin Landman
  • Fonction : Auteur
Thomas Petit-Jean
  • Fonction : Auteur
Gilles Chatellier
  • Fonction : Auteur
Kankoe Sallah
  • Fonction : Auteur
Richard Delorme
  • Fonction : Auteur

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. © 2024. The Author(s).
Fichier principal
Vignette du fichier
s44184-023-00046-7.pdf (1.28 Mo) Télécharger le fichier
Origine Publication financée par une institution

Dates et versions

hal-04752781 , version 1 (24-10-2024)

Licence

Identifiants

Citer

Romain Bey, Ariel Cohen, Vincent Trebossen, Basile Dura, Pierre-Alexis Geoffroy, et al.. Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality. npj Mental Health Research, 2024, 3 (1), ⟨10.1038/s44184-023-00046-7⟩. ⟨hal-04752781⟩
16 Consultations
2 Téléchargements

Altmetric

Partager

More