- The paper introduces a novel causal hidden Markov model that disentangles disease-related factors to improve forecasting accuracy.
- It reformulates a sequential variational auto-encoder to ensure identifiable features and mitigate spurious correlations.
- Experimental results on eye disease data show robust out-of-distribution generalization and precise detection of disease regions.
Introduction
In medical practice, forecasting diseases at an early stage is of great significance. Predictions made sufficiently early can enable timely treatments that may slow down or alleviate the progression of diseases, such as Alzheimer's Disease and specific eye conditions. Developing effective forecasting models is challenging, particularly due to incomplete information and changes in data distributions across populations, which can result in poor model generalization to new, out-of-distribution (OOD) samples.
Methodology
To address the challenges associated with forecasting irreversible diseases, researchers introduced a novel Causal Hidden Markov Model (Causal-HMM). The model features hidden variables that evolve over time and generate medical data at each time step. These hidden variables are divided into disease-related clinical and non-clinical parts, as well as a third category for other variables that may carry spurious correlations. By leveraging personal attributes and disease labels as supplementary information, the methodology ensures that disease-related hidden variables can be disentangled from other confounding factors. This disentanglement is pivotal for improving the model's viability on OOD medical data.
Algorithmic Framework and Theoretical Basis
A significant aspect of the approach is the reformulation of a sequential variational auto-encoder (VAE) that corresponds to the introduced causal model. This framework utilizes disentangled disease-related hidden variables to make predictions about the future occurrence of a disease. The authors provide a theoretical backbone, substantiated by an identifiability result, affirming that these carefully segregated features can indeed be learned without the interference of spurious correlations. Notably, this claim is consistent with the necessity of supervision—the disease label, clinical measurements, and personal attributes—to achieve valid feature disentanglement.
Experimental Validation
The efficacy of the Causal-HMM was tested on a dataset investigating peripapillary atrophy, a condition associated with irreversible eye diseases in children. The model was tasked with predicting the disease's presence or absence at the final stage (T) given data up to the preceding time steps. The results displayed improved accuracy and robust OOD generalization relative to existing methods. Moreover, an ablation paper highlighted the significance of each component's contribution to the model's performance, and visualization of the model's feature maps demonstrated precise identification of disease-related regions in medical images.
Conclusion
The paper presents a forward-looking approach in the arena of medical disease forecasting. By deliberately separating causative features from confounding data and ensuring their identifiability, Causal-HMM stands out as a robust method with improved prediction performance. The successful application on eye disease datasets signals potential for wider usage across varying medical contexts, with further extensions of this work to other diseases remaining an open avenue for exploration.