Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia
Clicks: 41
ID: 283629
2022
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
12.0
/100
41 views
17 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #619 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
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
| Reference Key |
kar2022using
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Thokozile Manaka; Terence Van Zyl; Alisha N Wade; Deepak Kar |
| Journal | arXiv |
| Year | 2022 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.