Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph
This work addresses the need for more accurate and explainable medical diagnosis systems for physicians, though it is incremental as it builds on existing knowledge graph and embedding methods.
The paper tackled the problem of incomplete medical knowledge graphs for diagnosis by refining them with latent representations from RDF2vec, resulting in improved predictions with additional valid conditions as validated through intrinsic and expert-based evaluations.
Medical diagnosis is the process of making a prediction of the disease a patient is likely to have, given a set of symptoms and observations. This requires extensive expert knowledge, in particular when covering a large variety of diseases. Such knowledge can be coded in a knowledge graph -- encompassing diseases, symptoms, and diagnosis paths. Since both the knowledge itself and its encoding can be incomplete, refining the knowledge graph with additional information helps physicians making better predictions. At the same time, for deployment in a hospital, the diagnosis must be explainable and transparent. In this paper, we present an approach using diagnosis paths in a medical knowledge graph. We show that those graphs can be refined using latent representations with RDF2vec, while the final diagnosis is still made in an explainable way. Using both an intrinsic as well as an expert-based evaluation, we show that the embedding-based prediction approach is beneficial for refining the graph with additional valid conditions.