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