Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies
This work addresses the problem of improving predictive models for patient readmission in healthcare, but it is incremental as it builds on a previously introduced framework.
The paper tackled the challenge of identifying predictive features in person-centric knowledge graphs for readmission prediction by conducting ablation studies on clinical, demographic, and social data from the MIMIC-III dataset, showing robustness in feature identification.
Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standardization or sparseness of the data. We previously introduced a person-centric ontology to organize information about individual patients, and a representation learning framework to extract person-centric knowledge graphs (PKGs) and to train Graph Neural Networks (GNNs). In this paper, we propose a systematic approach to examine the results of GNN models trained with both structured and unstructured information from the MIMIC-III dataset. Through ablation studies on different clinical, demographic, and social data, we show the robustness of this approach in identifying predictive features in PKGs for the task of readmission prediction.