Deep Neural Network-Based Feature Encoding for Automated Health Monitoring Using Large AI Models in Online Communication Systems

The hybrid model combines deep neural networks (DNN) and large AI models, such as large language models (LLM), for enhanced clinical information retrieval (CIR) from electronic clinical records (ECR). While LLMs show promise for encoding complex medical data, they face challenges in user-dependent information, such as patient reports with encoded knowledge, accessing real-time data, and requiring extensive fine-tuning for clinical decision-making in online communication systems. To overcome these limitations, we introduce a Transformer-based Sequence (TBS) multimodal method that integrates representation learning with human expertise to encode and analyze intricate relationships within clinical data. This model improves predictive tasks and medical search accuracy, achieving F1-scores of 0.83-0.80, and outperforms baseline methods. Integrating AI-driven methodologies in healthcare has the potential to transform medical record analysis and utilization, resulting in enhanced patient outcomes and more personalized healthcare solutions.