Accepted for/Published in: JMIR Formative Research
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Date Accepted:
Date Submitted to PubMed:
- Steve C, Agnes K, Oliver D
- I-DAIR CODEX: A Predictive Analytics No-Code Platform for Public Health Research in Africa
- JMIR Formative Research
- DOI: 10.2196/11848
- PMID: 30303485
- PMCID: 6352016
I-DAIR CODEX: A Predictive Analytics No-Code Platform for Public Health Research in Africa
Abstract
background
In the era of rapid digital evolution, the application of artificial intelligence (AI) and machine learning (ML) has emerged as a valuable strategy for organizations seeking to extract insights from large datasets. However, the adoption of AI and ML, traditionally associated with technical domains like computer science and data science, poses challenges for non-technical professionals such as public health researchers. To bridge this gap, we introduce I-DAIR CODEX, a no-code platform designed for end-to-end predictive analytics using AI/ML tools tailored specifically for public health researchers.
objective
In this paper, we describe the development process of building the I-DAIR CODEX for data analysis, machine learning, and predictive modelling for public health researchers.
methods
We actively engaged end users from the African Population and Health Research Center through a co-creation approach to iteratively define key feature requirements for the I-DAIR CODEX platform. We employed Agile software development methods in the platform's design and development.
results
Application of the I-DAIR CODEX platform demonstrated a substantial reduction in the time required for end-to-end predictive analytics tasks. In addition, the user feedback highlighted a high level of acceptance among the public health researchers, with respondents unanimously expressing that the application aligns seamlessly with their research requirements and holds the potential to be highly beneficial in their work. The feedback also emphasized the platform's user-friendly interface and the ease of task execution.
conclusions
To our knowledge, the I-DAIR CODEX is the first no-code platform designed for public health researchers within the African context. The platform is poised to empower public health researchers by facilitating the utilization of ML and AI technologies to extract insights from the data with reduced technical requirements.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it’s website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.
Citation
Please cite as:
Steve C, Agnes K, Oliver D
I-DAIR CODEX: A Predictive Analytics No-Code Platform for Public Health Research in Africa
JMIR Formative Research
DOI: 10.2196/14886
URL: https://preprints.jmir.org/preprint/11848
PMID: 31789598
PMCID: 6352016
I-DAIR CODEX: A Predictive Analytics No-Code Platform for Public Health Research in Africa
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