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In many lower-and-middle income countries including South Africa, data accessin health facilities is restricted due to patient privacy and confidentialitypolicies. Further, since clinical data is unique to individual institutions andlaboratories, 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) isoften determined by a verbal autopsy (VA) report in places without reliabledeath registration systems. A non-clinician field worker does a VA report usinga set of standardized questions as a guide to uncover symptoms of a COD. Thisanalysis focuses on the textual part of the VA report as a case study toaddress the challenge of adapting NLP techniques in the health domain. Wepresent a system that relies on two transfer learning paradigms of monolinguallearning and multi-source domain adaptation to improve VA narratives for thetarget task of the COD classification. We use the Bidirectional EncoderRepresentations from Transformers (BERT) and Embeddings from Language Models(ELMo) models pre-trained on the general English and health domains to extractfeatures from the VA narratives. Our findings suggest that this transferlearning system improves the COD classification tasks and that the narrativetext contains valuable information for figuring out a COD. Our results furthershow that combining binary VA features and narrative text features learned viathis framework boosts the classification task of COD.

Improving Cause-of-Death Classification from Verbal Autopsy Reports