Abstract
BackgroundComputer-coded verbal autopsy (CCVA) is a promising alternative to the standard approach of physician-certified verbal autopsy (PCVA), because of its high speed, low cost, and reliability. This study introduces a new CCVA technique and validates its performance using defined clinical diagnostic criteria as a gold standard for a multisite sample of 12,542 verbal autopsies (VAs).MethodsThe Random Forest (RF) Method from machine learning (ML) was adapted to predict cause of death by training random forests to distinguish between each pair of causes, and then combining the results through a novel ranking technique. We assessed quality of the new method at the individual level using chance-corrected concordance and at the population level using cause-specific mortality fraction (CSMF) accuracy as well as linear regression. We also compared the quality of RF to PCVA for all of these metrics. We performed this analysis separately for adult, child, and neonatal VAs. We also assessed the variation in performance with and without household recall of health care experience (HCE).ResultsFor all metrics, for all settings, RF was as good as or better than PCVA, with the exception of a nonsignificantly lower CSMF accuracy for neonates with HCE information. With HCE, the chance-corrected concordance of RF was 3.4 percentage points higher for adults, 3.2 percentage points higher for children, and 1.6 percentage points higher for neonates. The CSMF accuracy was 0.097 higher for adults, 0.097 higher for children, and 0.007 lower for neonates. Without HCE, the chance-corrected concordance of RF was 8.1 percentage points higher than PCVA for adults, 10.2 percentage points higher for children, and 5.9 percentage points higher for neonates. The CSMF accuracy was higher for RF by 0.102 for adults, 0.131 for children, and 0.025 for neonates.ConclusionsWe found that our RF Method outperformed the PCVA method in terms of chance-corrected concordance and CSMF accuracy for adult and child VA with and without HCE and for neonatal VA without HCE. It is also preferable to PCVA in terms of time and cost. Therefore, we recommend it as the technique of choice for analyzing past and current verbal autopsies.
Highlights
Verbal autopsy (VA) is a technique for measuring the cause-specific mortality burden for deaths that occur outside of hospitals
We developed an application of the Random Forest (RF) Method to VA analysis and compared the performance of RF to physician-certified verbal autopsy (PCVA)
The results shown are all for the one-versus-one model, with dichotomized variables, with training data reweighted to have equal class sizes, using the 40 most important Tariff-based symptoms per cause, m = 5, n = 100, and the max-rank cause assignment, which produced the highest cause-specific mortality fraction (CSMF) accuracy for seven of the first 200 splits of the child VA data with health care experience (HCE) and the highest chance-corrected concordance for 14
Summary
Verbal autopsy (VA) is a technique for measuring the cause-specific mortality burden for deaths that occur outside of hospitals. Physician-certified verbal autopsy (PCVA) is currently the most common approach to mapping VA interviews to underlying cause of death, but this approach is expensive and time-consuming [1]. One influential ML method, the artificial neural network (ANN), was applied to VA 10 years ago [3]. This approach was deemed potentially useful, pending further evaluation. This study introduces a new CCVA technique and validates its performance using defined clinical diagnostic criteria as a gold standard for a multisite sample of 12,542 verbal autopsies (VAs)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
