In many lower-and-middle income countries including South Africa, data access
in health facilities is restricted due to patient privacy and confidentiality
policies. Further, since clinical data is unique to individual institutions and
laboratories, there are insufficient data annotation standards and conventions.
As a result of the scarcity of textual data, natural language processing (NLP)
techniques have fared poorly in the health sector. A cause of death (COD) is
often determined by a verbal autopsy (VA) report in places without reliable
death registration systems. A non-clinician field worker does a VA report using
a set of standardized questions as a guide to uncover symptoms of a COD. This
analysis focuses on the textual part of the VA report as a case study to
address the challenge of adapting NLP techniques in the health domain. We
present a system that relies on two transfer learning paradigms of monolingual
learning and multi-source domain adaptation to improve VA narratives for the
target task of the COD classification. We use the Bidirectional Encoder
Representations from Transformers (BERT) and Embeddings from Language Models
(ELMo) models pre-trained on the general English and health domains to extract
features from the VA narratives. Our findings suggest that this transfer
learning system improves the COD classification tasks and that the narrative
text contains valuable information for figuring out a COD. Our results further
show that combining binary VA features and narrative text features learned via
this framework boosts the classification task of COD.

该研究针对医疗领域中的自然语言处理技术应用挑战，提出了一种通过转移学习来改进死因分类任务的方法，实验结果表明，将病程记录的文本信息作为特征来结合分类任务，可以有效提高死因诊断准确性。