Abstract

Mental health disorders represent a significant global health burden, often characterized by late diagnoses and inconsistent treatment outcomes. The integration of artificial intelligence (AI) with electronic health records (EHRs) offers a transformative approach to early diagnosis and intervention. This study presents the development of an AI-powered predictive model designed to enhance the diagnosis of mental health disorders by leveraging structured and unstructured data from EHRs. The model integrates natural language processing (NLP), machine learning (ML), and deep learning techniques to analyze clinical notes, patient histories, demographic data, and behavioral indicators. Our methodology involves preprocessing EHR datasets, extracting relevant features, and applying advanced algorithms such as random forests, support vector machines (SVM), and neural networks to build robust predictive models. Additionally, NLP techniques are used to process narrative text data, identifying critical indicators of mental health conditions such as depression, anxiety, bipolar disorder, and schizophrenia. The model is trained and validated using a large, anonymized EHR dataset, ensuring a high level of accuracy, precision, and recall in identifying at-risk individuals. Preliminary results demonstrate the model’s ability to outperform traditional diagnostic methods by identifying subtle patterns and risk factors often overlooked in standard clinical evaluations. Moreover, the integration of interpretability tools such as SHAP (SHapley Additive exPlanations) enables clinicians to understand the rationale behind each prediction, promoting trust and clinical applicability. This research underscores the potential of AI-driven tools in revolutionizing mental healthcare by enabling timely diagnosis, personalized treatment planning, and improved patient outcomes. However, the study also highlights challenges such as data quality, privacy concerns, algorithmic bias, and the need for interdisciplinary collaboration in deploying these systems responsibly. In conclusion, the proposed AI-powered predictive model demonstrates promise in augmenting mental health diagnostics using EHR data, paving the way for scalable and proactive mental healthcare delivery.