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.
