Flexible Bayesian Tensor Decomposition for Verbal Autopsy Data
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ID: 283600
2025
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Abstract
Cause-of-death data is fundamental for understanding population health trends
and inequalities as well as designing and evaluating public health
interventions. A significant proportion of global deaths, particularly in low-
and middle-income countries (LMICs), do not have medically certified causes
assigned. In such settings, verbal autopsy (VA) is a widely adopted approach to
estimate disease burdens by interviewing caregivers of the deceased. Recently,
latent class models have been developed to model the joint distribution of
symptoms and perform probabilistic cause-of-death assignment. A large number of
latent classes are usually needed in order to characterize the complex
dependence among symptoms, making the estimated symptom profiles challenging to
summarize and interpret. In this paper, we propose a flexible Bayesian tensor
decomposition framework that balances the predictive accuracy of the
cause-of-death assignment task and the interpretability of the latent
structures. The key to our approach is to partition symptoms into groups and
model the joint distributions of group-level symptom sub-profiles. The proposed
methods achieve better predictive accuracy than existing VA methods and provide
a more parsimonious representation of the symptom distributions. We show our
methods provide new insights into the clustering patterns of both symptoms and
causes using the PHMRC gold-standard VA dataset.
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| Authors | Yu Zhu; Zehang Richard Li |
| Journal | arXiv |
| Year | 2025 |
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