Real-Time Health Analytics Using Ontology-Driven Complex Event Processing and LLM Reasoning: A Tuberculosis Case Study

2025-10-05

AI Summary

Real-time detection of critical health events (e.g., tuberculosis) remains challenging in public health due to high-throughput, multi-source, heterogeneous medical data streams and weak semantic reasoning capabilities. Method: This paper proposes a real-time health analytics framework integrating ontology-driven Complex Event Processing (CEP) with Large Language Models (LLMs). It constructs an evolvable medical ontology grounded in the Basic Formal Ontology (BFO) and Semantic Web Rule Language (SWRL); ingests streaming data via Kafka and Spark Streaming; detects dynamic event patterns using a CEP engine; leverages LLMs for enhanced semantic understanding and interpretable reasoning; and employs an RDF knowledge graph to support knowledge-driven decision-making. Contribution/Results: Evaluated on a 1,000-case tuberculosis dataset, the system achieves an F1-score of 0.92 and end-to-end latency under 800 ms—demonstrating significant improvements in detection accuracy, timeliness, and reasoning interpretability. The framework exhibits strong scalability and clinical deployment potential.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Timely detection of critical health conditions remains a major challenge in public health analytics, especially in Big Data environments characterized by high volume, rapid velocity, and diverse variety of clinical data. This study presents an ontology-enabled real-time analytics framework that integrates Complex Event Processing (CEP) and Large Language Models (LLMs) to enable intelligent health event detection and semantic reasoning over heterogeneous, high-velocity health data streams. The architecture leverages the Basic Formal Ontology (BFO) and Semantic Web Rule Language (SWRL) to model diagnostic rules and domain knowledge. Patient data is ingested and processed using Apache Kafka and Spark Streaming, where CEP engines detect clinically significant event patterns. LLMs support adaptive reasoning, event interpretation, and ontology refinement. Clinical information is semantically structured as Resource Description Framework (RDF) triples in Graph DB, enabling SPARQL-based querying and knowledge-driven decision support. The framework is evaluated using a dataset of 1,000 Tuberculosis (TB) patients as a use case, demonstrating low-latency event detection, scalable reasoning, and high model performance (in terms of precision, recall, and F1-score). These results validate the system's potential for generalizable, real-time health analytics in complex Big Data scenarios.

Problem

• Detecting critical health conditions in real-time from diverse clinical data
• Integrating ontology-driven event processing with LLM reasoning for health analytics
• Enabling scalable semantic reasoning over high-velocity heterogeneous health data streams

Innovation

• Ontology-driven complex event processing for health analytics
• LLM reasoning for adaptive interpretation and ontology refinement
• Semantic RDF structuring with SPARQL querying for decisions