Medical Knowledge Extraction from Graph-Based Modeling of Electronic Health Records

Abstract

The variety and dimensionality of health-related data cannot be addressed by the human perception to arrive at useful knowledge or conclusions for proposing individualized treatment, diagnosis, or prognosis for a disease. Treating this wealth of heterogeneous data in a tabular manner deprives us of the knowledge that is hidden in interactions between the different types of data. In this paper, the potentials of graph-based data modeling and management are explored. Entities such as patients, encounters, observations, and immunizations are structured as graph elements with meaningful connections and are, consequently, encoded to form graph embeddings. The graph embeddings contain information about the graph structure in the vicinity of the node. This vicinity contains multiple low-level graph embeddings that are further encoded into a single high-level vector for utilization in downstream tasks by applying higher-order statistics on Gaussian Mixture Models With reference to the Covid-19 pandemic, we make use of synthetic data for predicting the risk of a patient’s fatality with a focus to prevent hospital overpopulation. Initial results demonstrate that utilizing networks of health data entities for the generation of compact medical representations has a positive impact on the performance of machine learning tasks. Since the generated Electronic Health Record vectors are label independent, they can be utilized for any classification or clustering task words.