用知识图谱和大模型实现实时结核病监测，响应快、准确率高。
Real-Time Health Analytics Using Ontology-Driven Complex Event Processing and LLM Reasoning: A Tuberculosis Case Study
- 结合事件处理与大模型推理，用本体建模医疗规则
- 1000例结核病数据测试，精确率、召回率、F1值均表现优异
- 适合需实时分析海量医疗数据的医疗机构
及时发现重大健康状况仍是公共卫生分析中的难题，尤其在数据量大、流速快、类型多样的大数据环境下。本研究提出一种基于本体的实时分析框架，融合复杂事件处理（CEP）与大语言模型（LLM），实现对异构高时效医疗数据流的智能事件检测与语义推理。系统采用基础形式本体（BFO）和语义网规则语言（SWRL）建模诊断规则与领域知识，通过Apache Kafka与Spark Streaming摄入并处理患者数据，由CEP引擎识别临床重要事件模式。LLM支持自适应推理、事件解释与本体迭代优化。临床信息以资源描述框架（RDF）三元组形式存入图数据库，支持SPARQL查询与知识驱动决策。以1000例结核病患者数据为案例评估，验证了系统低延迟事件检测、可扩展推理及高模型性能（精确率、召回率、F1分数），证明其在复杂大数据场景下具备通用性与实用性。
原文摘要 · Abstract (English)
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.
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