CHILLI project
OngoingPredictive Analytics for Earlier Identification of Healthcare Risk
This project will explore how routinely collected healthcare data can be used to identify patient or service-level risks earlier. The work will focus on responsible predictive modelling, interpretability and the practical needs of healthcare decision-makers.
About the project
This project will explore how routinely collected healthcare data can be used to identify patient or service-level risks earlier. The work will focus on responsible predictive modelling, interpretability and the practical needs of healthcare decision-makers.
Project team
Team members
David Olawade
MPH, FRSPH, SFHEA
A clinical researcher contributing expertise in healthcare improvement, evidence-informed decision-making, clinical evaluation, and responsible digital health innovation.
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Augustus Osborne
Research Fellow
Institute for Development (IfD)
Augustus Osborne is a public and global health researcher with expertise in health systems, maternal and child health, health equity,…
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Potential healthcare and academic partners to be confirmed.
Funding and support
Funding and data partnerships are being explored.
Research outputs
Publications and outputs
Using Explainable Machine Learning to Identify Predictors of Kangaroo Mother Care Implementation in Sierra Leone’s Healthcare System
European Journal of Integrative Medicine
Introduction Kangaroo Mother Care (KMC) reduces neonatal mortality and improves thermoregulation and breastfeeding, yet uptake remains inconsistent in Sierra Leone. Predictive and explainable tools could target implementation where…