  Background:Cognitive assessments represent the most common clinical routine
for the diagnosis of Alzheimer's Disease (AD). Given a large number of
cognitive assessment tools and time-limited office visits, it is important to
determine a proper set of cognitive tests for different subjects. Most current
studies create guidelines of cognitive test selection for a targeted
population, but they are not customized for each individual subject. In this
manuscript, we develop a machine learning paradigm enabling personalized
cognitive assessments prioritization. Method: We adapt a newly developed
learning-to-rank approach PLTR to implement our paradigm. This method learns
the latent scoring function that pushes the most effective cognitive
assessments onto the top of the prioritization list. We also extend PLTR to
better separate the most effective cognitive assessments and the less effective
ones. Results: Our empirical study on the ADNI data shows that the proposed
paradigm outperforms the state-of-the-art baselines on identifying and
prioritizing individual-specific cognitive biomarkers. We conduct experiments
in cross validation and level-out validation settings. In the two settings, our
paradigm significantly outperforms the best baselines with improvement as much
as 22.1% and 19.7%, respectively, on prioritizing cognitive features.
Conclusions: The proposed paradigm achieves superior performance on
prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top
have great potentials to facilitate personalized diagnosis, disease subtyping,
and ultimately precision medicine in AD.
