Published: Frontiers in Medicine

Congratulations to Dr Daniel Christiadi for his paper: Dynamic survival prediction of end-stage kidney disease using random survival forests for competing risk analysis being published in Fontiers in Medicine journal.

"Our paper presents a novel approach that combines survival analysis with the machine-learning algorithm Random Survival Forests to analyse serial clinical and pathological data. This method aims to dynamically predict the probability of developing end-stage kidney disease while also taking mortality into account. The current standard predictive models are limited to clinical data from a single time point, making them unsuitable for assessing patient risk over time. Our approach has been validated across distinct populations with a wide range of kidney function. By integrating clinical expertise with dynamic risk predictions, clinicians can have more informed discussions about whether high-risk patients should be prepared for dialysis." - Dr Daniel Christiadi

Read the paper here.