用自动构建的图结构统一多模态病历数据，提升阿尔茨海默病分类性能。
PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification tasks
- 将多源异构临床数据自动转为统一知识图谱表示
- 在ADNI-1数据集上实现98.5%平衡准确率和0.987的F1分数
- 无需预标准化即可支持可扩展的临床AI流程，适合医疗数据建模者
相较于单一模态或数据源，获取全面的多模态数据能显著提升人工智能在医学分类任务中的表现。然而，临床真实世界数据固有的异质性和复杂性给结构化分析与人工智能应用带来挑战，包括缺失值、多时间点、多种模态、格式与语义不一致等问题。数据整合前的数据标准化虽能缓解此问题，但耗时且易出错，限制了整体化、基于AI的临床决策支持系统的可扩展性与可复现性。为此，我们提出PatTree，一种基于图结构的患者全貌表示方法，可从真实临床数据中自动构建多模态数据的统一表示。PatTree无需依赖预标准化输入，即可实现早期数据融合，在统一知识图谱中保留跨模态与数据源间语义关系，促进互操作性与机器可读访问。基于ADNI-1队列子集（n = 763）的实验表明，直接在PatTree上进行分类已达到先进水平：在区分阿尔茨海默病、轻度认知障碍和正常认知三类人群的任务中，测试集平衡准确率达98.5%，F₁得分为0.987。结果表明，无需假设的自动化多模态医疗数据结构化可成为临床人工智能流水线的可扩展基础，避免繁琐的数据准备与标准化工作。
原文摘要 · Abstract (English)
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.
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