Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality
This is a virtual seminar. For a Zoom link, please see "Venue details". Please consider subscribing to mailing list: web.maillist.ox.ac.uk/ox/subscribe/ai4mch
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.
Date: 11 June 2024, 16:00 (Tuesday, 8th week, Trinity 2024)
Venue: https://zoom.us/j/91092603798?pwd=NUxQVGZ0SzY4OUR1TzRDOW9SdGQ2dz09
Speaker: Ariel Cohen (Assistance Publique-Hôpitaux de Paris (Paris, France))
Organising department: Department of Psychiatry
Organiser: Dr Andrey Kormilitzin (University of Oxford)
Organiser contact email address: andrey.kormilitzin@psych.ox.ac.uk
Host: Dr Andrey Kormilitzin (University of Oxford)
Part of: Artificial Intelligence for Mental Health Seminar Series
Booking required?: Not required
Booking url: https://web.maillist.ox.ac.uk/ox/info/ai4mch
Booking email: andrey.kormilitzin@psych.ox.ac.uk
Audience: Public
Editor: Andrey Kormilitzin