Quantifying the Contributions of Training Data and Algorithm Logic to the Performance of Automated Cause-Assignment Algorithms for Verbal Autopsy

Samuel J. Clark , The Ohio State University
Zehang Li, Yale University
Tyler McCormick, University of Washington, Seattle

A verbal autopsy (VA) consists of a interview with a relative or close contact of a decedent. VA surveys are commonly used to infer likely causes of death for individuals when deaths happen outside of hospitals or healthcare facilities. Several statistical and algorithmic methods are available to assign cause of death using VA data. Each requires as inputs some information about the joint distribution of symptoms and causes. We examine the generalizability of this symptom-cause information (SCI) by comparing different automated coding methods using various combinations of inputs and evaluation data. VA algorithm performance is affected by both the specific SCI themselves and the logic of a given algorithm. Using a variety of performance metrics for common VA algorithms, we demonstrate that in general the adequacy of the information about the joint distribution between symptoms and cause affects performance at least as much or more than algorithm logic.

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 Presented in Session 5. Health & Mortality 1