Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Age, sex, variables in the modified frailty index, Deyo's Charlson co-morbidity index ( ≥2), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. This was a retrospective observational study that included patients admitted to nine public hospitals for heart failure from Hong Kong between 20. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure. However, assessment of frailty status is time-consuming, and the electronic frailty indices developed using health records have served as useful surrogates. Frailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis.
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