Evaluation of trajectory analysis for disease risk assessment: a scoping review
Freya Pollington; Spiros Denaxas; Kezhi Li; Johan H. Thygesen; Georgios Lyratzopoulos; Becky L. White · 2025 · Journal of the American Medical Informatics Association
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
OBJECTIVES: Increasingly, structured longitudinal electronic health records (EHRs) are being harnessed to predict risk of having present but as yet undetected disease by analyzing "patient trajectories." Trajectory studies explore clinical event associations, characterize disease trajectories, and enhance risk prediction. This scoping review assesses study characteristics and objectives, identifies model types, and appraises model performance and reporting. MATERIALS AND METHODS: We conducted a scoping review, focused on a PubMed and Web of Science search for studies using temporal EHR sequenc
Abstract by Freya Pollington; Spiros Denaxas; Kezhi Li; Johan H. Thygesen; Georgios Lyratzopoulos; Becky L. White, Journal of the American Medical Informatics Association (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1093/jamia/ocaf208