Identification of at-risk mental health patients from Spanish clinical texts using Natural Language Processing

Early identification of at-risk patients is a key challenge in the field of mental health, particularly in contexts characterized by high demand for care and limited resources. In Paraguay, depressive disorders rank among the leading causes of outpatient consultations in public health services, underscoring the need for technological tools that support treatment continuity and clinical decision-making.

This Project proposes the development of an intelligent computational system that integrates Natural Language Processing (NLP) and Business Intelligence techniques to identify and monitor patients meeting risk criteria based on Spanish clinical texts.