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

arXiv 2204.12169 ↗First posted 26 Apr 2022cs.LG cs.CLcited by 3

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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

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.

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9 of 58 references.

References · 9

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  2. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  3. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  4. 2014Distributed Representations of Sentences and Documentscited by 678
  5. 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 493
  6. 2019Probing Biomedical Embeddings from Language Modelscited by 39
  7. 2020MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretrainingcited by 18
  8. 2016Neural Document Embeddings for Intensive Care Patient Mortality Predictioncited by 7
  9. 2014A Comparative Study of Machine Learning Methods for Verbal Autopsy Text Classificationcited by 2

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Citing papers per year: 2022: 3.

3 papers.

  1. 2022Improving Cause-of-Death Classification from Verbal Autopsy Reportscited by 1
  2. 2022Knowledge Graph Fusion for Language Model Fine-tuningcited by 0
  3. 2022Towards a methodology for addressing missingness in datasets, with an application to demographic health datasetscited by 0