Enhancing Mental Health Diagnostics: Implementing Convolutional Neural Networks and Natural Language Processing in AI-Based Assessment Tools

Abstract

This research paper explores the integration of Convolutional Neural Networks (CNNs) and Natural Language Processing (NLP) within artificial intelligence (AI) frameworks to enhance the diagnostics of mental health disorders. Traditional diagnostic methods often rely on subjective interpretation, which can lead to inconsistencies and delayed interventions. Our study proposes an AI-based assessment tool that leverages CNNs for image recognition in neuroimaging data, enhancing the identification of neurological patterns associated with various mental health conditions. Simultaneously, NLP is employed to analyze patient-reported outcomes and clinical notes, facilitating a more nuanced understanding of symptomatology and patient narratives. This dual approach aims to improve accuracy and timeliness in diagnosing disorders such as depression, anxiety, and bipolar disorder. We employed a dataset comprising both neuroimaging scans and a corpus of mental health records to train and validate our models. The CNN component showed robust performance in detecting anomalies with an accuracy rate surpassing conventional methods, while the NLP model demonstrated significant proficiency in identifying and categorizing clinical notes with high sensitivity and specificity. The integration of these technologies is discussed with respect to ethical considerations, data privacy, and the potential for personalized treatment pathways. Our findings suggest that the amalgamation of CNNs and NLP in AI-driven tools holds significant promise for transforming mental health diagnostics, offering a pathway to more adaptive and efficient healthcare solutions.