Predictive–collaborative model as recovery and validation tool. Case of study: Psychiatric emergency department decision support

There is a frequent situation in data mining where data collected must be used in real time to support decisions and they could present missing or non consistent values. The objective of this proposal consists of the recovery of missing values and verifies the consistency and integrity of the provided, in order to increase the information to support decisions. To address this, a predictive–collaborative model has been designed. It is composed of different predictive models generated by means of a training set and classifier selection algorithm. The combined suggestions of these predictive models are offered to support decisions. As case of study, the psychiatric emergency department at the Doce de Octubre Hospital in Madrid has been considered, where the response time is critical and the data are acquired in a stress situation which affects the quality of data significantly.