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

arXiv: https://arxiv.org/abs/2204.12169 · HTML: https://mlsanity.com/paper/2204.12169/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Probing Biomedical Embeddings from Language Models](https://mlsanity.com/paper/1904.02181/) (2019)
- [MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining](https://mlsanity.com/paper/2012.13978/) (2020)
- [Neural Document Embeddings for Intensive Care Patient Mortality Prediction](https://mlsanity.com/paper/1612.00467/) (2016)
- [A Comparative Study of Machine Learning Methods for Verbal Autopsy Text Classification](https://mlsanity.com/paper/1402.4380/) (2014)

## Built on by (most cited first, 3 of 3)

- [Improving Cause-of-Death Classification from Verbal Autopsy Reports](https://mlsanity.com/paper/2210.17161/) (2022)
- [Knowledge Graph Fusion for Language Model Fine-tuning](https://mlsanity.com/paper/2206.14574/) (2022)
- [Towards a methodology for addressing missingness in datasets, with an application to demographic health datasets](https://mlsanity.com/paper/2211.02856/) (2022)

## Abstract (arXiv)

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