학술논문
Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality.
Document Type
Academic Journal
Author
Bey R; Innovation and Data unit, IT Department, Assistance Publique-Hôpitaux de Paris, Paris, France.; Cohen A; Innovation and Data unit, IT Department, Assistance Publique-Hôpitaux de Paris, Paris, France. ariel.cohen-ext@aphp.fr.; Trebossen V; Child and Adolescent Psychiatry Department, Robert Debré University Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.; Dura B; Innovation and Data unit, IT Department, Assistance Publique-Hôpitaux de Paris, Paris, France.; Geoffroy PA; Département de psychiatrie et d'addictologie, GHU Paris Nord, DMU neurosciences, Bichat - Claude Bernard Hospital, Assistance Publique-Hôpitaux de Paris, 75018, Paris, France.; GHU Paris - psychiatry & neurosciences, 1, rue Cabanis, 75014, Paris, France.; NeuroDiderot, Inserm, FHU I2-D2, université Paris Cité, 75019, Paris, France.; CNRS UPR 3212, Institute for cellular and integrative neurosciences, 67000, Strasbourg, France.; Jean C; Innovation and Data unit, IT Department, Assistance Publique-Hôpitaux de Paris, Paris, France.; Université Paris-Est Créteil, INSERM, IMRB U955, Créteil, France.; Service Santé Publique & URC, Hôpital Henri Mondor, Assistance Publique-Hôpitaux de Paris, Créteil, France.; Landman B; Child and Adolescent Psychiatry Department, Robert Debré University Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.; Petit-Jean T; Innovation and Data unit, IT Department, Assistance Publique-Hôpitaux de Paris, Paris, France.; Chatellier G; Innovation and Data unit, IT Department, Assistance Publique-Hôpitaux de Paris, Paris, France.; Université Paris Cité, Paris, France.; Sallah K; URC PNVS, CIC-EC 1425, INSERM, Bichat - Claude Bernard Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.; Tannier X; Sorbonne Université, Inserm, Université Sorbonne Paris Nord, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé (LIMICS), Paris, France.; Bourmaud A; Université Paris Cité, Paris, France.; Clinical Epidemiology Unit, Robert Debré University Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.; CIC 1426, Inserm, Paris, France.; Delorme R; Child and Adolescent Psychiatry Department, Robert Debré University Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.; Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France.
Source
Publisher: Springer Nature Country of Publication: England NLM ID: 9918592488906676 Publication Model: Electronic Cited Medium: Internet ISSN: 2731-4251 (Electronic) Linking ISSN: 27314251 NLM ISO Abbreviation: Npj Ment Health Res Subsets: PubMed not MEDLINE
Subject
Language
English
Abstract
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).)
(© 2024. The Author(s).)