A few months ago I’ve participated in a Hackaton organized by Open Data Science on data from the Healthy Growth, Birth, Development knowledge integration (HBGDki) initiative by the Bill and Melinda Gates foundation.
The aim of this initiative is to collect data on child growth and development from several sources, to study which factors influence child growth and how to better intervene when there are risks. Currently the data comes from manual annotation of several publications, but future plans include launching a global effort to collect data systematically, and actually one of the objectives of the hackaton was to guide the planning of this effort.
I had a lot of fun during the hackaton and learned a lot. For me personally was an opportunity to learn more about the caret R package, which is a must-known library for doing machine learning in R. My plan for the hackaton was actually to do a trajectory clustering to see if there were different trajectories of growth of the baby during pregnancy, but unfortunately the analysis didn’t return very interesting results 🙂
See my github repo for some jupyter notebooks, and the slides on slideshare for more info.