Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and...

Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer's disease (AD), encompassing both conventional statistical methods and deep learning techniques.

Articles were sourced from PubMed, Embase and Web of Science databases using keywords related to dynamic prediction of AD. A set of criteria was developed to identify included studies. The correlation information for the construction of models was extracted.

We identified four methodological frameworks for dynamic prediction from 18 studies with two-stage model (n = 3), joint model (n = 11), landmark model (n = 2) and deep learning (n = 2). We reported and summarized the specific construction of models and their applications.

Each framework possesses distinctive principles and attendant benefits. The dynamic prediction models excel in predicting the prognosis of individual patients in a real-time manner, surpassing the limitations of traditional baseline-only prediction models. Future work should consider various data types, complex longitudinal data, missing data, assumption violations, survival outcomes, and interpretability of models.