Why the study?
Heart failure poses a significant threat to global health, and incorporating deep learning into survival prediction may augment predictive capacities and accuracy.
Does a DS-NN model improve survival prediction accuracy compared to traditional models in patients with advanced heart failure?
Does a DS-NN model improve survival prediction accuracy compared to traditional models in patients with advanced heart failure?
A deep learning model (DS-NN) demonstrated superior accuracy in predicting survival among patients with advanced heart failure compared to traditional statistical and machine learning models.
Answers are grounded in this paper and cite their sources.
DS-NN may aid HF survival prediction; leaves open external validation before clinical adoption.
Heart failure is a widespread cardiovascular ailment posing a significant threat to global health with an estimated 17.9 million annual fatalities. This study focuses on 299 patients with advanced heart failure (classified as III/IV) and left ventricular systolic dysfunction. Our examination involves assessing the concordance index for model evaluation. To augment our predictive capacities, we proposed a DS-NN. This model was compared against the random survival forest, gradient boosting, gradient boosting least square and the Cox proportional hazard model. Notably, DS-NN showcased superior prowess compared to the other five models with concordance index values of 0.73 and 0.72 for the training and testing sets, respectively. This implies that incorporating deep learning into survival prediction holds promise for more accuracy and offering clinicians’ valuable insights for treatment decisions. This ultimately leads to improved survival outcomes and the avoidance of unnecessary interventions.
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Ayesha et al. (2025) studied this question.
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