Causal Interpretable Progression Trajectory Analysis of Chronic Disease

Chronic disease is the leading cause of death, emphasizing the need for accurate prediction of disease progression trajectories and informed clinical decision-making. Machine learning (ML) models have shown promise in this domain by capturing non-linear patterns within patient features. However, existing ML-based models lack the ability to provide causal interpretable predictions and estimate treatment effects, limiting their decision-assisting perspective. In this study, we propose a novel model called causal trajectory prediction (CTP) to tackle the limitation. The CTP model combines trajectory prediction and causal discovery to enable accurate prediction of disease progression trajectories and uncovering causal relationships between features. By incorporating a causal graph into the prediction process, CTP ensures that ancestor features are not influenced by treatment on descendant features, thereby enhancing the interpretability of the model. By estimating the bounds of treatment effects, even in the presence of unmeasured confounders, the CTP provides valuable insights for clinical decision-making. We evaluate the performance of the CTP using simulated and real medical datasets. Experimental results demonstrate that our model achieves satisfactory performance, highlighting its potential to assist clinical decisions.