Random forests for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards
Random forests for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards
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DOI：
10.1186/1478-7954-9-29
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发表时间：
2011-08-04
影响因子：
3.3
通讯作者：
Murray, Christopher J. L.
中科院分区：
文献类型：
--
作者：
Flaxman, Abraham D.;Vahdatpour, Alireza;Murray, Christopher J. L.
背景：计算机编码的口头尸检 (CCVA) 是医生认证的口头尸检 (PCVA) 标准方法的一种有前景的替代方法，因为它速度快、成本低且可靠。本研究引入了一种新的 CCVA 技术，并使用定义的临床诊断标准作为 12,542 例口头尸检 (VA) 多站点样本的黄金标准来验证其性能。方法：采用机器学习 (ML) 中的随机森林 (RF) 方法来预测死因，方法是训练随机森林来区分每对原因，然后通过新颖的排序技术将结果组合起来。我们使用机会校正一致性在个体水平上评估了新方法的质量，并使用特定原因死亡率（CSMF）准确性和线性回归在群体水平上评估了新方法的质量。我们还对所有这些指标的 RF 质量与 PCVA 进行了比较。我们分别对成人、儿童和新生儿 VA 进行了分析。我们还评估了有或没有家庭医疗保健经历 (HCE) 回忆的情况下的表现差异。结果：对于所有指标、所有设置，RF 与 PCVA 一样好或更好，但具有 HCE 信息的新生儿的 CSMF 准确性不显着较低。对于 HCE，RF 的机会校正一致性成人高出 3.4 个百分点，儿童高出 3.2 个百分点，新生儿高出 1.6 个百分点。成人 CSMF 准确度高 0.097，儿童高 0.097，新生儿低 0.007。在没有 HCE 的情况下，成人 RF 的机会校正一致性比 PCVA 高 8.1 个百分点，儿童高 10.2 个百分点，新生儿高 5.9 个百分点。 RF 的 CSMF 准确度成人高出 0.102，儿童高出 0.131，新生儿高出 0.025。 结论：我们发现，在有和没有 HCE 的成人和儿童 VA 以及没有 HCE 的新生儿 VA 的机会校正一致性和 CSMF 准确度方面，我们的 RF 方法优于 PCVA 方法。就时间和成本而言，它也优于 PCVA。因此，我们建议将其作为分析过去和当前口头尸检的首选技术。
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 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).Results: For 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.Conclusions: We 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.