title: Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study.
abstract: The aim of this study was to determine whether an expert system based on automated processing of electronic health records (EHRs) could provide a more accurate estimate of the annual rate of emergency department (ED) visits for suicide attempts in France, as compared to the current national surveillance system based on manual coding by emergency practitioners. A feasibility study was conducted at Lyon University Hospital, using data for all ED patient visits in 2012. After automatic data extraction and pre-processing, including automatic coding of medical free-text through use of the Unified Medical Language System, seven different machine-learning methods were used to classify the reasons for ED visits into "suicide attempts" versus "other reasons". The performance of these different methods was compared by using the F-measure. In a test sample of 444 patients admitted to the ED in 2012 (98 suicide attempts, 48 cases of suicidal ideation, and 292 controls with no recorded non-fatal suicidal behaviour), the F-measure for automatic detection of suicide attempts ranged from 70.4% to 95.3%. The random forest and naïve Bayes methods performed best. This study demonstrates that machine-learning methods can improve the quality of epidemiological indicators as compared to current national surveillance of suicide attempts.
authors.affiliation: Université Paris 13 Sorbonne Paris Cité, Laboratoire Educations et Pratiques de Santé EA 3412, Bobigny, France.|Hospices Civils de Lyon, Hôpital de la Croix-Rousse, Unité d'hygiène et d'épidémiologie, Lyon, France.
authors.forename: Marie-Hélène
authors.initials: MH
authors.lastname: Metzger
authors.affiliation: Université Paris 13 Sorbonne Paris Cité, Laboratoire Educations et Pratiques de Santé EA 3412, Bobigny, France.
authors.forename: Nastassia
authors.initials: N
authors.lastname: Tvardik
authors.affiliation: Université de Lyon, Lyon, France.|CNRS UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Villeurbanne, France.
authors.forename: Quentin
authors.initials: Q
authors.lastname: Gicquel
authors.affiliation: Université de Lyon, Lyon, France.|CNRS UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Villeurbanne, France.
authors.forename: Côme
authors.initials: C
authors.lastname: Bouvry
authors.affiliation: Université de Lyon, Lyon, France.|Hospices Civils de Lyon, Groupement Hospitalier Edouard Herriot, Psychiatrie des Urgences, Lyon, France.|Université Lyon 1, EA 4615 «SIPAD» (Stimulation in Psychiatric and Addictives Disorders), Lyon, France.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Poulet
authors.affiliation: Hospices Civils de Lyon, Hôpital de la Croix-Rousse, Service des urgences, Lyon, France.
authors.forename: Véronique
authors.initials: V
authors.lastname: Potinet-Pagliaroli
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D062665:Epidemiological Monitoring; D005260:Female; D005602:France / Q000453:epidemiology; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D009323:Natural Language Processing; D010865:Pilot Projects; D012737:Sex Factors; D059020:Suicidal Ideation; D013406:Suicide, Attempted / Q000706:statistics & numerical data* / Q000639:trends; D055815:Young Adult
keywords: attempted suicide; emergency service; machine learning; natural language processing; population surveillance