发表机构
Institute for Biomedical Informatics, University of Cologne, Medical Faculty; University Hospital Cologne; Department of Data Science and Artificial Intelligence, Fraunhofer Institute for Applied Information Technology（科隆大学医学院生物医学信息学研究所; 科隆大学医院; 弗劳恩霍夫应用信息技术研究所数据科学与人工智能系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出PatTree，一种自动构建的多模态图基患者表示，在ADNI-1队列子集上实现阿尔茨海默病等三分类任务98.5%平衡准确率，可作为临床AI流程的可扩展基础。
AI 中文摘要
与使用单一模态或数据源相比，获取整体多模态数据可提升人工智能（AI）在医学分类任务中的性能。然而，临床真实世界数据固有的异质性和复杂性给结构化数据分析及AI应用带来重大挑战，这种异质性包括缺失值、多个时间点、多样模态、不一致的格式与语义。数据整合前的数据协调可应对该挑战，但仍资源密集且易出错，限制了基于临床真实世界数据的整体AI驱动决策支持的可扩展性和可重复性。因此，我们提出PatTree，一种基于图的患者整体表示，可通过多模态临床数据的自动结构化从真实世界临床数据中衍生而来。PatTree无需依赖预标准化输入即可实现早期数据整合，在统一知识图谱中表示异质临床数据的同时，保留跨模态和数据源的数据元素间的语义关系，促进互操作性和机器可解释的数据访问。我们使用ADNI-1队列的一个子集（n=763）进行验证，结果显示在PatTree上可直接进行患者分类，达到了先进的分类性能：在区分阿尔茨海默病、轻度认知障碍和认知正常个体的三分类任务中，我们在保留测试集上达到了98.5%的平衡准确率和0.987的F₁分数。我们的结果表明，对多模态医学数据进行无假设的自动结构化可作为临床AI流程的可扩展基础，绕过繁琐的数据准备和标准化步骤。
英文摘要
Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing single modalities or data sources. However, the inherent heterogeneity and complexity of clinical real-world data pose significant challenges to structured data analysis and AI application. This heterogeneity includes missing values, multiple time points, diverse modalities, and inconsistent formats and semantics. Data harmonization prior to data integration tackles this challenge but remains resource-intensive and error-prone, limiting the scalability and reproducibility of holistic, AI-driven decision support on clinical real-world data. We therefore propose PatTree, a graph-based, holistic representation of patients that can be derived from real-world clinical data through the automated structuring of multimodal clinical data. PatTree enables early-stage data integration without relying on pre-standardized inputs. While representing heterogeneous clinical data within a unified knowledge graph, PatTree preserves the semantic relationships between data elements across modalities and data sources, facilitating interoperability and machine-interpretable data access. Using a subset of the ADNI-1 cohort (n = 763), we demonstrate that classification of patients is directly feasible on PatTree reaching state-of-the-art classification performance. In the three-class classification task distinguishing Alzheimer's disease, mild cognitive impairment, and cognitively normal individuals, we achieve a balanced accuracy of 98.5% and an F$_1$ score of 0.987 on the held-out test set. Our results show that assumption-free, automated structuring of multimodal medical data can serve as a scalable foundation for clinical AI pipelines bypassing tedious data preparation and standardization.
