Measuring Data Collection Quality for Community Healthcare
Year: 2020
Type: preprint
Abstract: Machine learning has tremendous potential to provide targeted interventionsin low-resource communities, however the availability of high-quality public
health data is a significant challenge. In this work, we partner with field
experts at a non-governmental organization (NGO) in India to define and test a
data collection quality score for each health worker who collects data. This
challenging unlabeled data problem is handled by building upon domain-expert's
guidance to design a useful data representation that is then clustered to infer
a data quality score. We also provide a more interpretable version of the
score. These scores already provide for a measurement of data collection
quality; in addition, we also predict the quality for future time steps and
find our results to be very accurate. Our work was successfully field tested
and is in the final stages of deployment in Rajasthan, India.
Source: arXiv (Cornell University)
Language: English
Topic: Data Quality and Management
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3097724056