[{"key":"dc.contributor.author","value":"Rahimi, Mahan","language":null},{"key":"dc.date.accessioned","value":"2026-04-15T21:41:28Z","language":null},{"key":"dc.date.available","value":"2026-04-15T21:41:29Z","language":null},{"key":"dc.date.issued","value":"2026","language":"en"},{"key":"dc.identifier.uri","value":"http:\/\/hdl.handle.net\/2429\/94095","language":null},{"key":"dc.description.abstract","value":"Sepsis remains a major cause of preventable pediatric mortality in low- and middle-income countries, where early identification of high-risk children is challenged by limited health worker availability, high patient volumes, and variability in frontline assessments. This thesis aims to develop and validate data-driven tools for early in-hospital risk stratification using routinely obtainable admission data from children under five admitted with suspected sepsis across six Ugandan hospitals (mortality 7% in the 0\u20136-month cohort; 4.1% in the 6\u201360-month cohort). We conducted a secondary analysis evaluating three modeling streams: photoplethysmography (PPG)-only models based on ten features extracted from one-minute infrared waveforms, clinical-only models using eight simple clinical and anthropometric variables, and combined clinical+PPG models integrating both data sources. Across all streams, model development employed repeated nested cross-validation, strict separation of internal and external cohorts, and model-agnostic interpretability techniques. \r\nPPG-only models demonstrated, for the first time to our knowledge, that brief, low-cost waveforms captured at admission could encode clinically meaningful signatures predictive of mortality, though performance was moderate and sensitive to probe-related domain shift. Clinical-only models achieved stronger and more stable discrimination, retaining ranking ability during temporal external validation despite lower outcome prevalence. Combined clinical+PPG models yielded the highest overall performance, improving discrimination, precision\u2013recall metrics, and decision-analytic utility relative to unimodal approaches. These models also reduced false positives at fixed sensitivity thresholds, offering operational advantages in resource-constrained settings. External validation revealed heterogeneous generalizability: probe heterogeneity impaired PPG-only models in younger children, whereas combined models were more resilient and performed well in cohorts with harmonized data-collection pipelines.\r\nTaken together, these findings demonstrate the feasibility and promise of integrating physiological waveforms and simple clinical measures into machine learning models for early pediatric risk stratification in resource-constrained hospitals. By leveraging rapid, low-cost, and objective admission data, the models developed in this thesis provide a foundation for scalable digital decision-support tools that may strengthen triage, reduce resource misallocation, and support timely escalation of care. Prospective evaluation and broader external validation are needed before clinical implementation.","language":"en"},{"key":"dc.language.iso","value":"eng","language":"en"},{"key":"dc.publisher","value":"University of British Columbia","language":"en"},{"key":"dc.rights","value":"Attribution-NonCommercial-NoDerivatives 4.0 International","language":"*"},{"key":"dc.rights.uri","value":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/","language":"*"},{"key":"dc.title","value":"From admission to outcome : mortality prediction in Ugandan children with suspected infection using clinical and photoplethysmography features","language":"en"},{"key":"dc.type","value":"Text","language":"en"},{"key":"dc.degree.name","value":"Doctor of Philosophy - PhD","language":"en"},{"key":"dc.degree.discipline","value":"Biomedical Engineering","language":"en"},{"key":"dc.degree.grantor","value":"University of British Columbia","language":"en"},{"key":"dc.contributor.supervisor","value":"Dumont, G. (Guy), 1951-","language":null},{"key":"dc.contributor.supervisor","value":"Ansermino, J. Mark","language":null},{"key":"dc.date.graduation","value":"2026-05","language":"en"},{"key":"dc.type.text","value":"Thesis\/Dissertation","language":"en"},{"key":"dc.description.affiliation","value":"Applied Science, Faculty of","language":"en"},{"key":"dc.description.affiliation","value":"Biomedical Engineering, School of","language":"en"},{"key":"dc.degree.campus","value":"UBCV","language":"en"},{"key":"dc.description.scholarlevel","value":"Graduate","language":"en"}]