SOURCE-LINKED INTELLIGENCE

Development and Validation of ‘findtb’, A Novel Digital Tool for Tuberculosis Triage in Children - Using Innovative Methodologies and an Interdisciplinary Approach

CORDIS · observation · Publication date unknown

Starting with large, existing geographically and demographically representative data sets, my team and I will develop and validate two predictive models using highly innovative data science methods (machine learning and Bayesian techniques). An app for use by health personnel at all levels of the health care system will be developed for the best performing model. To maximize the app’s implementation potential, I will engage key stakeholders to assess preferences, acceptability, and feasibility prior to and throughout app development. Furthermore, I will apply human-centered design principles. We will then evaluate the design-locked digital tool in a real-world triage use case in a prospective, cross-sectional diagnostic accuracy study. A cost-effectiveness analysis of population level impact will round out a dossier that will inform a WHO review. This efficient, effective, and scalable digital tool for TB triage of children in resource-limited settings is expected to improve TB diagnosis on an individual-level and thus TB-related morbidity and mortality and on population-level is li

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recordType

award

status

SIGNED

region

EU

value

1999788

unit

EUR