aceOne-day-ahead prediction of severe hyponatremia in patients with cancer using medication and diagnostic trajectories without electrolyte indicators
Intro Hyponatremia is a common complication in patients with cancer and is associated with poor prognosis; however, early detection remains challenging. Existing models often rely on laboratory parameters directly related to sodium levels, limiting early risk identification before electrolyte testing. This study developed a machine learning model to predict severe hyponatremia 1 day before its occurrence using longitudinal prescription and diagnostic data without relying on serum electrolyte variables. Methods T…
A gradient-boosting model using only 30-day prescription and diagnosis trajectories predicted severe hyponatremia one day ahead with an AUC of 0.853, close to a lab-inclusive model's 0.862.
The study is retrospective and from a single center, and the authors state that its use for targeted testing and earlier intervention depends on external validation.
Evidence
- Peer-reviewedInternational Journal of Medical Informatics2026-10-07
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Truvace Impact Record TRV-2026-1745, v1: “One-day-ahead prediction of severe hyponatremia in patients with cancer using medication and diagnostic trajectories without electrolyte indicators.” Truvace, 2026-10-17. /record/TRV-2026-1745 (accessed at citation time). sha256 a8622427d6585980…
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