Journal Article Research Support, Non-U.S. Gov't Ppublish 4 citations
PubMed: 36803841 DOI: 10.1016/j.psychres.2023.115090
Text mining methods for the characterisation of suicidal thoughts and behaviour.
Psychiatry Res 322:115090
Authors: Alba Sedano-Capdevila, Mauricio Toledo-Acosta, María Luisa Barrigon, Eliseo Morales-González, David Torres-Moreno, Bolívar Martínez-Zaldivar, Jorge Hermosillo-Valadez, Enrique Baca-García
Affiliations
- Department of Psychiatry, University Hospital Rey Juan Carlos, Mostoles, Spain.
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, 62209 Cuernavaca, Morelos, México.
- Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain; Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain.
- Department of Psychiatry, University Hospital Rey Juan Carlos, Mostoles, Spain; Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain; Department of Psychiatry, General Hospital of Villalba, Madrid, Spain; Department of Psychiatry, University Hospital Infanta Elena, Valdemoro, Spain; Department of Psychiatry, Madrid Autonomous University, Madrid, Spain; CIBERSAM (Centro de Investigación en Salud Mental), Carlos III Institute of Health, Madrid, Spain; Universidad Católica del Maule, Talca, Chile; Department of psychiatry. Centre Hospitalier Universitaire de Nîmes, France.
Abstract
Traditional research methods have shown low predictive value for suicidal risk assessments and limitations to be applied in clinical practice. The authors sought to evaluate natural language processing as a new tool for assessing self-injurious thoughts and behaviors and emotions related. We used MEmind project to assess 2838 psychiatric outpatients. Anonymous unstructured responses to the open-ended question "how are you feeling today?" were collected according to their emotional state. Natural language processing was used to process the patients' writings. The texts were automatically represented (corpus) and analyzed to determine their emotional content and degree of suicidal risk. Authors compared the patients' texts with a question used to assess lack of desire to live, as a suicidal risk assessment tool. Corpus consists of 5,489 short free-text documents containing 12,256 tokenized or unique words. The natural language processing showed an ROC-AUC score of 0.9638 when compared with the responses to lack of a desire to live question. Natural language processing shows encouraging results for classifying subjects according to their desire not to live as a measure of suicidal risk using patients' free texts. It is also easily applicable to clinical practice and facilitates real-time communication with patients, allowing better intervention strategies to be designed.
MeSH
Psychiatry, Natural Language Processing, Risk Assessment