Flexible Bayesian Tensor Decomposition for Verbal Autopsy Data

Music City Center 

Cause-of-death data is crucial for understanding health trends and guiding public health interventions, especially in low- and middle-income countries where many deaths lack medically certified causes. Verbal autopsy (VA) is commonly used in these settings to estimate disease burdens by interviewing caregivers of the deceased. Traditional models for VA data analysis often involve complex latent class models that are difficult to interpret due to the need for a large number of latent classes to capture symptom dependencies. We propose a flexible Bayesian tensor decomposition framework that enhances both the interpretability of latent structures and the accuracy of cause-of-death assignments. By grouping symptoms and modeling their interactions, our approach simplifies the analysis and improves understanding of symptom and cause clustering. This method shows improved predictive accuracy and offers a more parsimonious representation of symptoms compared to existing models, as demonstrated with synthetic data and the PHMRC gold-standard VA dataset.

Bayesian hierarchical model;

probabilistic tensor decomposition;

ause-of-death classification;

verbal autopsy;

mortality quantification 

Section on Bayesian Statistical Science