Random forests for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards
Published August 31, 2011, in Population Health Metrics (opens in a new window)
The traditional method of assigning causes of death to verbal autopsies (VAs), physician-certified verbal autopsy (PCVA), has been shown to have varying accuracy. Computer-coded verbal autopsy (CCVA) is a promising alternative to the standard approach of PCVA because of its high speed, low cost, and reliability. An innovative method of CCVA, the Random Forest (RF) method from machine learning, was found to outperform PCVA in almost all settings, according to a study by researchers from IHME and the Bill & Melinda Gates Foundation as part of the Population Health Metrics Research Consortium (PHMRC).
Flaxman AD, Vahdatpour A, Green S, James SL, Murray CJL, the Population Health Metrics Research Consortium (PHMRC). Random Forests for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards. Population Health Metrics. 2011; 9:29.