Causal Heterogeneous Graph Learning Method for Chronic Obstructive Pulmonary Disease Prediction
This addresses the problem of low screening rates and high burden in COPD for healthcare, but it is incremental as it combines existing techniques like causal inference with heterogeneous graph learning.
The paper tackles early identification and warning of acute exacerbation in Chronic Obstructive Pulmonary Disease (COPD) by developing a Causal Heterogeneous Graph Representation Learning (CHGRL) method, which achieves high detection accuracy compared to strong GNN baselines.
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.