Health Domain

Lacuna Fund health datasets reduce health disparities by helping providers and patients make decisions that lead to more equitable healthcare outcomes. These datasets can be used to train chatbots, provide reliable medical information to the public, assist with disease screening and diagnosis, and assess the health and treatment of large populations over time (e.g. maternal health or HIV data). Learn more and download released datasets below.

2021 Awards: Equity and Health

Machine Learning Dataset for Rabies Diagnosis and Outbreak Prediction

Description: This dataset will help in the real-time and remote diagnosis of rabies disease for humans and animals in low-resource settings. A time series approach can be applied to the outbreak dataset to predict the number of rabies cases likely to occur within an area after a given time interval. This approach can help with resource mobilization, too, such as identifying the number of vaccines required in a specific area at a given time. The number of observations from the two datasets is 12,684. There are three datasets for rabies diagnosis for animals and humans, with 7,081 and 4,585 observations, respectively. In the outbreak prediction dataset, 1,018 observations were accounted for.

Contact: Asa Emmanuel | asakalonga@gmail.com and Kennedy Lushasi | klushasi@ihi.or.tz

Authors and Affiliations: Asa Emmanuel, Rebecca Chaula, Deogratias Mzurikwao, Joel Changalucha, Kennedy Lushasi

Dataset: access here.

All Lacuna Fund datasets are licensed under the CC-BY 4.0 International license unless otherwise noted.