Random forests for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards.

ABSTRACT:

Background

Computer-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).

Methods

The 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 mortali

SUBMITTER: Flaxman AD 

PROVIDER: S-EPMC3160922 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature