Abstract

The early detection of cognitive concerns is crucial for timely intervention and improved patient outcomes. However, analyzing large-scale real-world data for cognitive decline presents significant challenges in efficiency and accuracy. This paper introduces an Autonomous AI Framework that leverages machine learning and natural language processing (NLP) to identify cognitive concerns from diverse datasets, including electronic health records (EHRs), social media interactions, and clinical notes. Our approach integrates deep learning models, feature selection techniques, and interpretability methods to enhance detection accuracy and provide actionable insights. Experimental results demonstrate the framework’s effectiveness in identifying early signs of cognitive issues with high precision and recall.