MD-Manifold：一种用于医疗概念与患者表征的基于医疗距离的表征学习方法
MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation
AI总结：
针对现有医疗概念与患者表征方法存在的动态模式捕捉不足、难融入领域知识等问题，提出MD-Manifold框架，通过数据增强、概念距离度量等结合流形学习生成表征，在下游医疗任务中性能优于现有技术，兼具理论与实践价值。
摘要：
有效表征医疗概念与患者对医疗分析应用至关重要。为医疗分析任务表征医疗概念需要整合医疗领域知识与患者描述数据中的先验信息。当前的特征工程、将医疗概念映射至标准化术语表等方法，在捕捉患者描述数据中的动态模式方面存在局限。其他基于嵌入的方法难以融入重要的医疗领域知识，且通常需要大量训练数据，这对多数医疗系统而言并不可行。\n我们提出的框架MD-Manifold为医疗概念与患者表征提供了一种新方法。它包含一种新型数据增强方法、概念距离度量以及患者-患者网络，用以整合关键的医疗领域知识与先验数据信息。随后它采用流形学习方法，生成能准确反映医疗知识的医疗概念级表征，以及可清晰识别异质患者群体的患者级表征。在多项下游医疗分析任务中，MD-Manifold的表现也优于其他现有最优技术。\n本研究对信息系统领域的表征学习、知识驱动型机器学习研究，以及将设计科学作为下游探索性与预测性分析的中间框架具有重要意义。在实践层面，MD-Manifold有望通过整合医疗领域知识与先验数据信息，生成有效且泛化性强的医疗概念与患者表征。它能深化对医疗数据的洞察，助力开发新型分析应用以提升医疗效果。
英文摘要：
Effectively representing medical concepts and patients is important for healthcare analytical applications. Representing medical concepts for healthcare analytical tasks requires incorporating medical domain knowledge and prior information from patient description data. Current methods, such as feature engineering and mapping medical concepts to standardized terminologies, have limitations in capturing the dynamic patterns from patient description data. Other embedding-based methods have difficulties in incorporating important medical domain knowledge and often require a large amount of training data, which may not be feasible for most healthcare systems. Our proposed framework, MD-Manifold, introduces a novel approach to medical concept and patient representation. It includes a new data augmentation approach, concept distance metric, and patient-patient network to incorporate crucial medical domain knowledge and prior data information. It then adapts manifold learning methods to generate medical concept-level representations that accurately reflect medical knowledge and patient-level representations that clearly identify heterogeneous patient cohorts. MD-Manifold also outperforms other state-of-the-art techniques in various downstream healthcare analytical tasks. Our work has significant implications in information systems research in representation learning, knowledge-driven machine learning, and using design science as middle-ground frameworks for downstream explorative and predictive analyses. Practically, MD-Manifold has the potential to create effective and generalizable representations of medical concepts and patients by incorporating medical domain knowledge and prior data information. It enables deeper insights into medical data and facilitates the development of new analytical applications for better healthcare outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
