Minería de datos espaciales como herramienta de mejora en la asignación de recursos en Salud Pública: Un enfoque basado en evidencias

Abstract

Public institutions should raise awareness and train their public servants on the importance of innovation. In this sense, we present some results related to a Master's Final Work focused on the implementation of supervised and unsupervised spatial data mining (SDM) techniques for the PAMI repository. Data mining is a technology that seeks to discover meaningful knowledge in different domains, such as health. The objective of this article is to generate knowledge to support decision making at managerial levels for efficient resource management. The CRISP-DM methodology was adapted. The application of spatial data mining can help to extract useful patterns that lack naked eye visibility. The current results of the implementation have proven to be highly effective in analyzing the characteristics of the affiliates and the distribution of their pathologies. On the other hand, they have made it possible to identify areas where a greater presence of the agency is required. In short, they have facilitated the generation of knowledge to support technical decision-making in the field of health and social security.