BREAKING SILOS, BUILDING SOLUTIONS: A SOFAR FELLOW’S EXPERIENCE IN MACHINELEARNING FOR HEALTH

The Health Reserch Unit hosted a three-day Introduction to Machine Learning for Healthcare course, bringing together clinicians, researchers, and data enthusiasts from across Zimbabwe.

For SOFAR Fellow Cyprian Masvikeni, the workshop underscored the power of interdisciplinary collaboration in addressing real-world health challenges. “The diversity in the room was incredible,” Cyprian shared. “Bringing together different perspectives made the learning experience richer and highlighted the immense potential of cross-disciplinary collaboration.” Led by PhD students from Imperial College London, the course blended foundational theory with hands on practice using real-world datasets.

Participants explored how to build and evaluate predictive models, with a strong emphasis on practical applications in clinical settings. “The practical sessions were particularly valuable,” Cyprian noted. “Working with real data made it clear how machine learning can support better decision-making in healthcare.” A panel discussion featuring Professor Payam Barnaghi (Imperial College London) and Professor Tawanda Mushiri (Scientific and Industrial Research and Development Centre) further grounded the training in real world context. The discussion sparked important conversations on ethical AI, data governance, and the challenges of implementation in healthcare systems.

Reflecting on the experience, Cyprian emphasized the broader impact: “This wasn’t just about learning new skills — it was about rethinking how we can use data collaboratively to improve health outcomes, especially for vulnerable populations like newborns.