MindWatch: A Smart Cloud-based AI solution for Suicide Ideation Detection leveraging Large Language Models

Abstract

AbstractSuicide, a serious public health concern affecting millions of individuals worldwide, refers to the intentional act of ending one’s own life. Mental health issues such as depression, frustration, and hopelessness can directly or indirectly influence the emergence of suicidal thoughts. Early identification of these thoughts is crucial for timely diagnosis. In recent years, advances in artificial intelligence (AI) and natural language processing (NLP) have paved the way for revolutionizing mental health support and education. In this proof-of-concept study, we have created MindWatch, a cutting-edge tool that harnesses the power of AI-driven language models to serve as a valuable computer-aided system for the mental health professions to achieve two important goals such asearly symptom detection, and personalized psychoeducation. We utilized ALBERT and Bio-Clinical BERT language models and fine-tuned them with the Reddit dataset to build the classifiers. We evaluated the performance of bi-LSTM, ALBERT, Bio-Clinical BERT, OpenAI GPT3.5 (via prompt engineering), and an ensembled voting classifier to detect suicide ideation. For personalized psychoeducation, we used the state-of-the-art Llama 2 foundation model leveraging prompt engineering. The tool is developed in the Amazon Web Service environment. All models performed exceptionally well, with accuracy and precision/recall greater than 92%. ALBERT performed better (AUC=.98) compared to the zero-shot classification accuracies obtained from OpenAI GPT3.5 Turbo (ChatGPT) on hidden datasets (AUC=.91). Furthermore, we observed that the inconclusiveness rate of the Llama 2 model is low while tested for few examples. This study emphasizes how transformer models can help provide customized psychoeducation to individuals dealing with mental health issues. By tailoring content to address their unique mental health conditions, treatment choices, and self-help resources, this approach empowers individuals to actively engage in their recovery journey. Additionally, these models have the potential to advance the automated detection of depressive disorders.