Guardian-BERT: Early detection of self-injury and suicidal signs with language technologies in electronic health...

Mental health disorders, including non-suicidal self-injury (NSSI) and suicidal behavior, represent a growing global concern. Early detection of these conditions is crucial for timely intervention and prevention of adverse outcomes. In this study, we present Guardian-BERT (Guardian-Bidirectional Encoder Representations from Transformers), a novel approach for the early detection of NSSI and suicidal behavior in electronic health records (EHRs) using natural language processing (NLP) techniques for the Spanish language. Guardian-BERT employs a dual-domain adaptation strategy based on a pre-trained language model. The initial adaptation phase involves training on EHR discharge reports, enabling the model to learn the structure and linguistic patterns typical of clinical text. A second adaptation phase, using EHRs from the Psychiatry department of another hospital, refines the model's understanding of the specialized terminology and nuanced expressions used by mental health professionals. Empirical results show that Guardian-BERT outperforms existing pre-trained models and other supervised methods in detecting NSSI and suicidal behavior. The model achieves a more balanced trade-off between precision and recall, resulting in superior F-measure scores. Specifically, Guardian-BERT attains an F-measure of 0.95 for NSSI detection and 0.89 for suicidal behavior prediction. In addition to predictive performance, we investigated risk factors associated with these mental health conditions, identifying influences such as adverse personal circumstances and emotional distress. This analysis serves two key purposes: enhancing the interpretability of individual predictions by linking them to relevant risk factors, and enabling broader research through patient stratification and temporal studies of risk factor evolution. Our findings indicate that language technologies like Guardian-BERT offer valuable support for healthcare professionals by facilitating early detection and prevention of mental health disorders. Furthermore, the integration of risk factor analysis provides critical insights into the underlying conditions, improving both the explainability and clinical utility of predictive systems.