Text mining methods for the characterisation of suicidal thoughts and behaviour

• Published in: Psychiatry research 2023-04, Vol.322, p.115090-115090, Article 115090
• Main Authors: Sedano-Capdevila, Alba, Toledo-Acosta, Mauricio, Barrigon, María Luisa, Morales-González, Eliseo, Torres-Moreno, David, Martínez-Zaldivar, Bolívar, Hermosillo-Valadez, Jorge, Baca-García, Enrique, Aroca, Fuensanta, Artes-Rodriguez, Antonio, Berrouiguet, Sofian, Billot, Romain, Carballo-Belloso, Juan Jose, Courtet, Philippe, Gomez, David Delgado, Lopez-Castroman, Jorge, Rodriguez, Mercedes Perez, Aznar-Carbone, Julia, Cegla, Fanny, Gutiérrez-Recacha, Pedro, Izaguirre-Gamir, Leire, Herrera-Sanchez, Javier, Borja, Marta Migoya, Palomar-Ciria, Nora, Martínez, Adela Sánchez-Escribano, Vasquez, Manuel, Vallejo-Oñate, Silvia, Vera-Varela, Constanza, Amodeo-Escribano, Susana, Arrua, Elsa, Bautista, Olga, Barrigón, Maria Luisa, Carmona, Rodrigo, Caro-Cañizares, Irene, Carollo-Vivian, Sonia, Chamorro, Jaime, González-Granado, Marta, Iza, Miren, Jiménez-Giménez, Mónica, López-Gómez, Ana, Mata-Iturralde, Laura, Miguelez, Carolina, Muñoz-Lorenzo, Laura, Navarro-Jiménez, Rocío, Ovejero, Santiago, Palacios, María Luz, Pérez-Fominaya, Margarita, Peñuelas-Calvo, Inmaculada, Pérez-Colmenero, Sonia, Rico-Romano, Ana, Rodriguez-Jover, Alba, SánchezAlonso, Sergio, Sevilla-Vicente, Juncal, Vigil-López, Carolina, Villoria-Borrego, Lucía, Martin-Calvo, Marisa, Alcón-Durán, Ana, Stasio, Ezequiel Di, García-Vega, Juan Manuel, Martín-Calvo, Pedro, Ortega, Ana José, Segura-Valverde, Marta, Bañón-González, Sara María, Crespo-Llanos, Edurne, Codesal-Julián, Rosana, Frade-Ciudad, Ainara, Merino, Elena Hernando, Álvarez-García, Raquel, Coll-Font, Jose Marcos, Portillo-de Antonio, Pablo, Puras-Rico, Pablo, Serrano-Marugán, Leticia
• Format: Article
• Language: English
• Subjects: Data Mining; Emotions; Humans; Machine learning; Mobile health; Natural language processing; Outpatients; Suicidal Ideation; Suicide attempt; Suicide, Attempted - psychology; Suicide, Suicidal ideation
• ISSN: 0165-1781; 1872-7123
• DOI: 10.1016/j.psychres.2023.115090

•NLP is promising tool in suicide risk assessment overcoming limitations of traditional assessment.•We compare NLP with the answer to a question about “lack of desire to live” to classify suicide risk.•The words used to divide patients into groups correlate with usual clinical observations.•NLP showed with a ROC-AUC score of 0.964 to classify patients according suicidal risk. 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.