###### Abstract  
This study presents a next-generation clinical decision-support architecture for Clinical Decision Support Systems (CDSS) focused on emergency triage. By integrating Large Language Models (LLMs), Federated Learning (FL), and low-latency streaming analytics within a modular, privacy-preserving framework, the system addresses key deployment challenges in high-stakes clinical settings. Unlike traditional models, the architecture processes both structured (vitals, labs) and unstructured (clinical notes) data to enable context-aware reasoning with clinically acceptable latency at the point of care. It leverages big data infrastructure for large-scale EHR management and incorporates digital twin concepts for live patient monitoring. Federated training allows institutions to collaboratively improve models without sharing raw data, ensuring compliance with GDPR/HIPAA, and FAIR principles. Privacy is further protected through differential privacy, secure aggregation, and inference isolation. We evaluate the system through two studies: (1) a benchmark of 750+ USMLE-style questions validating the medical reasoning of fine-tuned LLMs; and (2) a real-world case study (\(n=132\), 75.8% first-pass agreement) using de-identified MIMIC-III data to assess triage accuracy and responsiveness. The system demonstrated clinically acceptable latency and promising alignment with expert judgment on reviewed cases. The infectious disease triage case demonstrates low-latency recognition of sepsis-like presentations in the ED. This work offers a scalable, audit-compliant, and clinician-validated blueprint for CDSS, enabling low-latency triage and extensibility across specialties. Clinical decision support; triage; fair data principles; generative AI; large language models; federated learning; privacy preservation; healthcare architecture; infectious disease triage; sepsis alerting +
Footnote †: journal: Applied Sciences  
increasingly difficult. This challenge has accelerated interest in Clinical Decision Support Systems (CDSS) that integrate artificial intelligence (AI) to support fast, consistent, and evidence-based decisions. CDSS have increasingly been recognized for their potential to enhance clinical outcomes by delivering timely, evidence-based recommendations directly at the point of care. Modern CDSS implementations leverage advanced machine learning algorithms, such as deep learning, to interpret complex, multimodal healthcare data--ranging from structured EHR data to unstructured clinical narratives--thereby improving diagnostic accuracy and treatment effectiveness while minimizing clinician cognitive load [1]. Early applications of machine learning to structured EHR data showed promising results in predicting outcomes such as mortality and readmission [2]. [PERSON] et al. demonstrated that deep neural networks could model hospital outcomes with high accuracy when trained on large EHR datasets [3]. A comprehensive tutorial by [PERSON] et al. summarized early successes and pitfalls of deep learning in healthcare [4]. However, most of these models were limited in their ability to handle unstructured clinical narratives--notes, referrals, and patient reports--which often contain critical diagnostic information. Simultaneously, concerns about data privacy and cross-institutional collaboration have constrained the centralization of health data. Regulations such as GDPR (in the EU) [5] and HIPAA (in the U.S.) [6] restrict sharing of identifiable patient information [7; 8; 9]. Federated Learning (FL) emerged as a viable solution, enabling collaborative model training without transferring raw data between hospitals. [PERSON] et al. demonstrated the feasibility of FL in training deep learning models across multiple hospitals for brain tumor segmentation, matching centralized performance while preserving privacy [7]. More recently, Large Language Models (LLMs) have shown strong capabilities in understanding and generating clinical language, even passing medical licensing exams [10; 11].