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

Domain:

healthcarenatural language processing

Record type:

paper
Creator:
ManVanWadKar
Publisher:
arXiv
Host:avatar
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 13 pages, 5 figures, Southern African Conference for Artificial Intelligence Research

Visit

doi.orgarxiv.org

Tasks

text classification

Tags

Machine Learning (cs.LG)Computation and Language (cs.CL)FOS: Computer and information sciencesFOS: Computer and information sciences

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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In countries without civil registration and vital statistics, verbal autopsy (VA) is a critical tool

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BACKGROUND: The most common method for determining cause of death is certification by physicians ba

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Abstract Background Verbal autopsies (VAs) collect information on deaths in low an