Multi-Context Mining-Based Graph Neural Network for Predicting Emerging Health Risks

Abstract

Patients with similar diseases are able to have similar treatments, care, symptoms, and causes. Based on these relations, it is possible to predict latent risks. Therefore, this study proposes Graph Neural Network-based Multi-Context mining for predicting emerging health risks. The proposed method collects and pre-processes chronic disease patients' disease information, behavioral pattern information, and mental health information. After that, it performs context mining. This is a multivariate regression analysis for predicting multiple dependent variables, it extracts a regression model and generates a feature map. Then, the initial graph is created by defining the number of clusters as nodes and constructing edges through correlation. By expanding the graph according to the results of context mining, it is possible to predict that a user has a similar chronic disorder and similar symptoms through users' connection relations. For performance evaluation, the validity of the regression analysis of context mining used in the proposed method, and the suitability of the clustering technique are evaluated.

ViT-Based Multi-Scale Classification Using Digital Signal Processing and Image Transformation

“…Precision represents the ratio of true positive items among the items classified as positive by the model. The Precision metric is used to measure how accurately a model predicts a positive class [36]. Recall represents the percentage of items correctly classified as positive by a model among actual positive items.…”