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

  • Recommending scholarly articles to monitor COVID-19 trends in social media based on low-cost topic modeling (Active)

  • MeSH2Ontology: Machine learning-driven biomedical ontology creation 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