In this presentation we will describe a low-code approach to implementing machine learning with DHIS2 tracker data and the results of a pilot study.
Leveraging DHIS2 API to pulling and pushing (and visualizing) metadata changes across versions for easier roll-back options post upgrade.
The team at CHAI in collaboration with the program is creating a central warehouse that eliminates the data silos and can leverage machine learning
The study seeks to propose a mathematical formula for forecasting malaria cases in 5 years. This is to support decision making by Health authorities
Using DHIS2 logs in order to analyze data use
Machine learning models to aid the Ministry of Health in South Africa in planning and implementing initiatives to reach Universal Health Coverage by 2030.
How we can design a data analytic platform to analyze data (structured and unstructured) coming from different sources including DHIS2
The immunization program leverages data stored in DHIS2 to model a data lake solution that aims to improve service delivery outcomes and vaccine access.
The presentation will use real-world data to highlight challenges with analyzing surveillance data and demonstrate methods to infer the underlying data trends.
