Project Title: Establishment of machine learning database for batter health outcomes: A focus on rabies
Project Description: Artificial intelligence (AI) and machine learning are tools with potential to address many challenges in the healthcare sector in low- and middle-income countries (LMICs), however there is slow adoption of these approaches . This is due to both a lack of machine learning skills limited resources such as the availability of training. This project aims to address these two setbacks with focus on rabies, a neglected zoonotic disease that kills thousands of people every year... Artificial intelligence (AI) and machine learning are tools with potential to address many challenges in the healthcare sector in low- and middle-income countries (LMICs), however there is slow adoption of these approaches . This is due to both a lack of machine learning skills limited resources such as the availability of training. This project aims to address these two setbacks with focus on rabies, a neglected zoonotic disease that kills thousands of people every year in LMCs. We aim to provide high-0quality datasets on rabies ready machine learning applications. The datasets will be created from our existing systems for rabies surveillance that includes Integrated Bite Case Management (IBCM) and contact tracing. These two system contain extensive data from 2010 onward with over 15,000 bite patients records, and over 3,000 animal rabies cases and are accompanied by archived brain samples from animals and whole genome sequence for a proportion of positive cases. The datasets will be annotated and labelled, ready for machine learning in diagnosis of rabies from reported clinical signs, prediction of the timing and spread of outbreaks and demand for costly human rabies vaccines. The proposed datasets will be made publicly accessible for researchers, students, and practitioners for and responsible use to address the burden of rabies.
Principal Investigator : Kennedy Lushasi
Department Name :
Time frame: (2022-07-01) - (2023-07-31)
None added yet.
External Collaborating Partners
Muhimbili University of Health and Allied Sciences (MUHAS)