aceImproving Haemorrhage Prediction in General Ward Patients by Incorporating Patterns of Change Derived From Routine Ward Measurements
To evaluate whether patterns of change derived from routine ward measurements provide additional predictive value beyond the most recent clinical measurements for haemorrhage prediction in general ward patients. A retrospective cohort study. Among 16,197 adult patients admitted to general wards in three university-affiliated hospitals in South Korea, 1152 (7.1%) experienced haemorrhage, whereas 15,045 (92.9%) did not. Three machine-learning algorithms evaluated three feature sets based on the most recent clinica…
Machine-learning models that add patterns of change in routine ward measurements to the latest values predicted haemorrhage slightly better than models using the latest values alone.
The models were only internally validated in a retrospective design, and the gains were small, so the authors say prospective evaluation is needed to establish clinical utility.
Evidence
- Peer-reviewedJournal of Clinical Nursing2026-10-25
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Truvace Impact Record TRV-2026-2269, v1: “Improving Haemorrhage Prediction in General Ward Patients by Incorporating Patterns of Change Derived From Routine Ward Measurements.” Truvace, 2026-10-26. /record/TRV-2026-2269 (accessed at citation time). sha256 b9a6a08432b69295…
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