Advancement in Healthcare System Analysis using Deep Learning Architecture: Extended Study with Recent Developments in Neural Networks and Knowledge Graph Integration

The growing complexity of modern healthcare systems requires intelligent deep learning frameworks capable of efficiently processing large-scale, multi-modal medical data while ensuring data privacy, interpretability, and real-time responsiveness. However, existing healthcare knowledge management systems continue to face major challenges, including high processing latency, limited contextual understanding of clinical narratives, inadequate semantic reasoning, lack of transparency in AI-driven decisions, and concerns regarding the secure handling of sensitive patient information. In response to these challenges, this study introduces KADLA 2.0, an enhanced and scalable extension of the original Knowledge Acquisition Deep Learning Architecture (KADLA). While the initial KADLA framework established a strong foundation for distributed deep learning and causal knowledge mapping in healthcare environments, it was unable to fully address issues related to real-time inference, clinical interpretability, and privacy-preserving collaborative learning. To overcome these limitations, KADLA 2.0 incorporates recent advancements in deep learning, semantic intelligence, and secure distributed computation. The proposed framework replaces conventional WordNet-based natural language processing mechanisms with transformer-based biomedical language models, namely ClinicalBERT and BioBERT, enabling deeper contextual understanding of medical terminology and clinical narratives. This enhancement directly addresses the research problem of insufficient semantic comprehension in traditional NLP systems. Furthermore, to improve semantic reasoning and relationship discovery, the framework integrates RotatE-based knowledge graph embeddings, which effectively model complex medical ontologies and support accurate prediction of hidden clinical associations. Another significant challenge in healthcare AI is the “black-box” nature of predictive models, which reduces trust among clinicians and healthcare practitioners. To address this issue, KADLA 2.0 incorporates Explainable Artificial Intelligence (XAI) techniques, including SHAP and LIME, allowing healthcare professionals to interpret and validate model predictions with greater confidence. In addition, the framework adopts a Federated Learning strategy to solve the problem of centralized data dependency and privacy risks. This decentralized approach enables collaborative model training across multiple healthcare institutions without exposing raw patient data, thereby ensuring compliance with international regulations such as HIPAA and GDPR. Experimental evaluation demonstrates the effectiveness of the proposed solutions when compared with the baseline KADLA framework. KADLA 2.0 achieves a 23% improvement in precision and an 18% increase in recall, indicating enhanced predictive performance and clinical relevance. Moreover, the framework reduces processing latency by 92%, lowering response time from 4200 ms to 340 ms, which enables near real-time clinical inference. These findings confirm that KADLA 2.0 provides a robust, interpretable, privacy-aware, and scalable solution for advanced healthcare applications, including Electronic Health Records (EHR), Clinical Decision Support Systems (CDSS), and Population Health Management.