Initiatives + Projects
Initiatives
An initiative is a theme / general direction / collection of goals that act as a guide for community projects and have an open-ended time frame
Projects
Projects are our fundamental unit of collaborative work
Each project has at least one project lead to spearhead activity and aims to achieve a specific outcome
Projects can be started by anyone in the community and can be aligned with multiple initiatives
Join any community meet
to start or participate in a project
(",)
Initiatives
State of Machine Learning and Health in Africa
Goals
Toward better understanding of the Machine Learning and Healthcare community, research, industry, interests and problems in Africa
Projects
Survey paper on Machine Learning and Health in Africa (Active)
Discoverable African Health Research
Goals
Toward developing machine learning tools to help make health related research more discoverable
Projects
Recommender system for scholarly articles to monitor COVID-19 trends in social media based on low-cost topic modeling (Complete accepted to HIS)
MeSH2KG: Machine learning-driven biomedical relation extraction based on the MeSH keywords of PubMed scholarly publications (Active)
MeSH2Matrix: Machine learning-driven biomedical relation classification based on the MeSH keywords of PubMed scholarly publications (Complete accepted to BIR@ECIR)
Data models for annotating biomedical texts: the case of CORD-19 (Complete accepted to Sci-K@WWW)
Accessible African Health Datasets
Goals
Toward making African health datasets more accessible while preserving privacy and sovereignty
Projects
Privacy Preserving AI for African Health datasets (Active)
Machine Learning and Health Community Development
Goals
Help build the SisonkeBiotik community
Help bootstrap other communities for machine learning and health
Projects
SisonkeBiotik Seminars (Active)
No communities currently incubating - please reach out to us via sisonkebiotik@gmail.com if you would like help bootstrapping your community
Projects
Survey paper on Machine Learning and Health in Africa
OutcomeSurvey paper
Project leadsChris Fourie (contact on Discord @ Chris Fourie#5230)Houcemeddine Turki (contact on Discord @ csisc#7682)Chris Emezue (contact @ Chris Emezue#8673)
Description To address problems relating to machine learning and health in Africa, we first have to understand what problems exist and who is already working on them.
Entrypoints
- Help with academic paper writing
Privacy Preserving AI for African Health datasets
OutcomeUse privacy preserving AI tools to make an initial African health dataset accessible
Project leadsArchie Arakkal (contact on Discord @ Archie#9168)Chris Fourie (contact on Discord @ Chris Fourie#5230)
Description Approach African health data custodians (hospitals, universities, research groups) to help make their data more accessible using privacy preserving AI.
Entrypoints
- Help with code base
- Help with academic paper writing
- Looking for help with process of ethical approval around Privacy Preserving AI
MeSH2Matrix
Outcomes
- Academic paper on Machine learning-driven biomedical relation classification based on the MeSH keywords of PubMed scholarly publications (Complete accepted to BIR@ECIR)
Project leadsHoucemeddine Turki (contact on Discord @ csisc#7682)Bonaventure Dossou (contact on Discord @bona.dossou#3457 Chris Emezue (contact @ Chris Emezue#8673)
Description At the information age, many semantic resources are freely made available online. These resources include bibliographic databases (e.g., PubMed), taxonomies (e.g., MeSH), ontologies (e.g., Disease Ontology), and knowledge graphs (e.g., Wikidata). However, scientists tend to exclusively use advanced machine learning techniques for developing computer applications in biomedical data science. These techniques require a lot of human capacities and funding to work. So, they are not well adapted to the African context characterized by the scarcity of its support to research. Given that the datasets that are used for training machine learning models generally have semantic values (e.g., Electronic Health Records, Biomedical Publications), semantic resources can be embedded to algorithms allowing knowledge-aware and explainable machine learning with a limited complexity. This will allow users to achieve interesting accuracy for biomedical informatics applications at a low cost.
Entrypoints
- Help with code base
- Help with dataset creation
- Help with academic paper writing
- Propose biomedical informatics approaches based on knowledge resouces
Roadmap
Identify gaps towards the use of knowledge resources in biomedical applications
Develop algorithms to enhance the development of knowledge-based systems in biomedicine
Create methods and datasets to evaluate and adjust knowledge-based approaches
Validate proposed approaches for using knowledge resources in biomedical informatics