Tang Zhenyou, Tang Zhong, Wu Jing
Institute of Collaborative Innovation, University of Macau, Macau, China.
College of Humanities and Social Sciences, Guangxi Medical University, Guangxi, Nanning, China.
Front Bioeng Biotechnol. 2025 Aug 12;13:1644697. doi: 10.3389/fbioe.2025.1644697. eCollection 2025.
Recent advances in artificial intelligence have created opportunities for medical anomaly detection through multimodal learning frameworks. However, traditional systems struggle to capture the complex temporal and semantic relationships in clinical data, limiting generalization and interpretability in real-world settings.
To address these challenges, we propose a novel framework that integrates symbolic representations, a graph-based neural model (PathoGraph), and a knowledge-guided refinement strategy. The approach leverages structured clinical records, temporally evolving symptom graphs, and medical ontologies to build semantically interpretable latent spaces. Our method enhances model robustness under sparse supervision and distributional shifts.
Extensive experiments across electronic health records and diagnostic datasets show that our model outperforms existing baselines in detecting rare comorbidity patterns and abnormal treatment responses.
Additionally, it improves interpretability and trustworthiness, which are critical for clinical deployment. By aligning domain knowledge with multimodal AI, our work contributes a generalizable and explainable solution to healthcare anomaly detection.
人工智能的最新进展为通过多模态学习框架进行医学异常检测创造了机会。然而，传统系统难以捕捉临床数据中复杂的时间和语义关系，限制了在现实环境中的泛化能力和可解释性。
为应对这些挑战，我们提出了一种新颖的框架，该框架集成了符号表示、基于图的神经模型（PathoGraph）和知识引导的优化策略。该方法利用结构化临床记录、随时间演变的症状图和医学本体来构建语义可解释的潜在空间。我们的方法在稀疏监督和分布变化下增强了模型的鲁棒性。
在电子健康记录和诊断数据集上进行的大量实验表明，我们的模型在检测罕见共病模式和异常治疗反应方面优于现有基线。
此外，它提高了可解释性和可信度，这对于临床应用至关重要。通过将领域知识与多模态人工智能相结合，我们的工作为医疗保健异常检测提供了一种可推广且可解释的解决方案。
