Lower-and-middle income countries are faced with challenges arising from a
lack of data on cause of death (COD), which can limit decisions on population
health and disease management. A verbal autopsy(VA) can provide information
about a COD in areas without robust death registration systems. A VA consists
of structured data, combining numeric and binary features, and unstructured
data as part of an open-ended narrative text. This study assesses the
performance of various machine learning approaches when analyzing both the
structured and unstructured components of the VA report. The algorithms were
trained and tested via cross-validation in the three settings of binary
features, text features and a combination of binary and text features derived
from VA reports from rural South Africa. The results obtained indicate
narrative text features contain valuable information for determining COD and
that a combination of binary and text features improves the automated COD
classification task.
Keywords: Diabetes Mellitus, Verbal Autopsy, Cause of Death, Machine
Learning, Natural Language Processing

本研究旨在评估机器学习方法在分析结构化和非结构化叙述文本组成的言语验尸报告时的表现，结果表明文本特征对于确定死亡原因具有重要的信息价值，并且结合二进制和文本特征可以提高自动化 COD 分类任务的准确度。

使用机器学习将口述验尸叙述和二进制特征融合在高血糖死亡分析中

Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia

In regions without complete-coverage civil registration and vital statistics
systems there is uncertainty about even the most basic demographic indicators.
In such areas the majority of deaths occur outside hospitals and are not
recorded. Worldwide, fewer than one-third of deaths are assigned a cause, with
the least information available from the most impoverished nations. In
populations like this, verbal autopsy (VA) is a commonly used tool to assess
cause of death and estimate cause-specific mortality rates and the distribution
of deaths by cause. VA uses an interview with caregivers of the decedent to
elicit data describing the signs and symptoms leading up to the death. This
paper develops a new statistical tool known as InSilicoVA to classify cause of
death using information acquired through VA. InSilicoVA shares uncertainty
between cause of death assignments for specific individuals and the
distribution of deaths by cause across the population. Using side-by-side
comparisons with both observed and simulated data, we demonstrate that
InSilicoVA has distinct advantages compared to currently available methods.

通过使用口头尸检工具，本文开发了一种名为 InSilicoVA 的新的统计工具来确定死因，并展示了它相对于目前可用方法的明显优势，能够在未完成民事登记和关键统计系统的地区帮助评估死亡率和死因分布。