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.

翻译：有效表征医学概念和患者对于医疗分析应用至关重要。为医疗分析任务表征医学概念需要整合医学领域知识以及患者描述数据中的先验信息。当前方法（如特征工程和将医学概念映射至标准化术语）在捕捉患者描述数据的动态模式方面存在局限性。其他基于嵌入的方法难以纳入重要的医学领域知识，且通常需要大量训练数据，这对大多数医疗系统而言难以实现。我们提出的框架MD-Manifold引入了一种新颖的医学概念与患者表征方法，包含新的数据增强方法、概念距离度量以及患者-患者网络，以整合关键的医学领域知识和先验数据信息。随后，该方法适应流形学习技术生成准确反映医学知识的医学概念级表征，以及清晰识别异质性患者队列的患者级表征。MD-Manifold在各种下游医疗分析任务中的表现亦优于其他前沿技术。本研究对表征学习、知识驱动机器学习，以及将设计科学作为下游探索性与预测性分析中间框架的信息系统研究具有重大意义。在实际应用中，MD-Manifold通过整合医学领域知识与先验数据信息，有望创建有效且泛化的医学概念与患者表征，从而深入挖掘医疗数据洞察，推动开发新的分析应用以改善医疗成效。