Title: Quantifying the Contributions of Training Data and Algorithm Logic to the Performance of Automated Cause-assignment Algorithms for Verbal Autopsy,
Abstract: A verbal autopsy (VA) consists of a survey with a relative or close contact
of a person who has recently died. 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 surveys. Each of these methods
require as inputs some information about the joint distribution of symptoms and
causes. In this note, we examine the generalizability of this symptom-cause
information 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 all existing 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.