RT Journal Article
T1 Data mining process to detect suicidal behaviour in out-of-hospital emergency departments
A1 Del-Campo-Ávila, José
A1 Ramos Martín, Javier
A1 Gómez-Sánchez-Lafuente, Carlos
A1 García-Pedrosa, Johanna
A1 García-Martín, Saúl
A1 Martínez-García, Ana I.
A1 Guzmán-Parra, José
A1 Morales-Bueno, Rafael
A1 Moreno-Kustner, Berta
K1 Gestión de emergencias - Proceso de datos
K1 Suicidio - Proceso de datos
AB Out-of-hospital emergency departments receive multiple types of requests daily. Their management requires a balance to be found between available resources and the actual needs of the requesting party. Those regarding suicidal behaviour, which are resource heavy, are few in number in terms of the bulk of requests, and detecting them correctly is therefore important. Previous research, using machine learning algorithms to analyse suicide, has typically focused on discovering insights to be used by medical personnel. This proposal extends its use in two directions: knowledge that can be used by non-exclusively medical staff, such as telephone operators, and the models that have been incorporated into a software prototype to help in the decision-making of an emergency department. In addition, previous research has often included a range of information from different sources that are not available when processing an emergency call request, for example, data that is only obtained at the end of the intervention. A full-scale data mining process has been performed using data from the out-of-hospital emergency service in Malaga (Spain). Sensitivity has been the primary goal to avoid missing cases requiring special attention, but this objective has been pursued without overlooking a good trade-off with specificity. The best models can offer such a compromise between sensitivity and specificity, and show more than 80% in both metrics simultaneously. The experts validate that the modelling phase showed that the algorithms have automatically identified already known situations. This lays the groundwork for further iterations with a promising outlook.
PB Elsevier
YR 2024
FD 2024-07-13
LK https://hdl.handle.net/10630/32118
UL https://hdl.handle.net/10630/32118
LA eng
NO This study was funded by the Fundación Progreso y Salud (Junta de Andalucía, Spain). Number: AP-0226-2019. Funding for open access charge: Universidad de Málaga/CBUA, Spain.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026