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STAT Seminar Series: A Latent Class Model for Verbal Autopsies

Speaker Name: 
Zehang Richard Li
Speaker Title: 
Assistant Professor in the Department of Statistics
Speaker Organization: 
University of California Santa Cruz
Start Time: 
Monday, November 23, 2020 - 4:00pm
End Time: 
Monday, November 23, 2020 - 5:05pm
Via Zoom Presentation
Visit. Asst. Prof. Claudia Wehrhahn


Verbal autopsy (VA) is a survey-based tool for assigning a cause to deaths when traditional autopsy and cause certification are not available. It has been routinely used for mortality surveillance in low-resource settings. In the last decade, several statistical and machine learning methods for inferring cause-of-death using VA data have been developed. Generalizability has been a common challenge with most of these probabilistic VA algorithms, as data collected from different domains, e.g., locations or time periods, often exhibit different relationships between causes and symptoms. As a result, the choice of training data has strong implications on the performance of VA algorithms. In this talk, I will present statistical approaches to characterize the joint distribution of symptoms while accounting for the heterogeneity of data from different domains. We propose a novel latent class model that classifies causes-of-death by learning the similarities between the new domain and the existing domains. I will demonstrate the performance and interpretability of the method using a gold-standard VA dataset collected from multiple study sites.

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