Talk / Overview

Using machine learning, we succeeded in improving the quality of health care for children in Burkina Faso, a country in which one in ten children dies before the age of 5. Our Integrated e-Diagnostic Approach (IeDA) programme helps Frontline Health Workers (FHW) with the diagnosis and treatment of childhood diseases, while providing authorities with nation-wide statistics. By performing an anomaly detection analysis on millions of IeDA consultations, we identified different classes of mistakes (for example, in weight, height and respiratory frequency measurements) made by FHW, that can drive erroneous diagnoses. The workers can now receive targeted coaching, thereby improving healthcare for the youngest and most vulnerable children in Burkina Faso.

Talk / Speakers

Aziza Merzouki

Data Scientist, University of Geneva

Talk / Slides

Download the slides for this talk.Download ( PDF, 54025.22 MB)

Talk / Highlights


Improving the quality of child consultations in Burkina Faso using ML: detecting and addressing Health Workers’ diagnostic mistakes

With Aziza MerzoukiPublished March 12, 2020

AMLD / Global partners