  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.
