Causal Heterogeneous Graph Learning Method for Chronic Obstructive Pulmonary Disease Prediction

TL;DR

This paper introduces a novel Causal Heterogeneous Graph Representation Learning method for predicting COPD risk, integrating causal inference with graph learning to improve early detection and management of the disease.

Contribution

The study develops a new causal heterogeneous graph learning architecture that combines causal inference mechanisms with graph learning for COPD risk prediction.

Findings

High detection accuracy demonstrated in experiments

Effective integration of causal reasoning in graph models

Outperforms strong GNN baselines

Abstract

Due to the insufficient diagnosis and treatment capabilities at the grassroots level, there are still deficiencies in the early identification and early warning of acute exacerbation of Chronic obstructive pulmonary disease (COPD), often resulting in a high prevalence rate and high burden, but the screening rate is relatively low. In order to gradually improve this situation. In this paper, this study develop a Causal Heterogeneous Graph Representation Learning (CHGRL) method for COPD comorbidity risk prediction method that: a) constructing a heterogeneous Our dataset includes the interaction between patients and diseases; b) A cause-aware heterogeneous graph learning architecture has been constructed, combining causal inference mechanisms with heterogeneous graph learning, which can support heterogeneous graph causal learning for different types of relationships; and c) Incorporate the causal loss function in the model design, and add counterfactual reasoning learning loss and causal regularization loss on the basis of the cross-entropy classification loss. We evaluate our method and compare its performance with strong GNN baselines. Following experimental evaluation, the proposed model demonstrates high detection accuracy.