Artificial Intelligence (AI)‐based dynamic predictions of longitudinal and survival data of Alzheimer's disease

Abstract

Background: Dynamically predicting patients at risk of Alzheimer's disease (AD) is crucial for timely treatment and care in precision medicine. Method: We present a novel architecture, a Kolmogorov‐Arnold networks (KANs)‐based joint prediction model of longitudinal and survival data (JM‐KAN).

The paper