We propose an innovative machine learning paradigm enabling precision medicine for prioritizing cognitive assessments according to their relevance to Alzheimer's disease at the individual patient level. The paradigm tailors the cognitive biomarker discovery and cognitive assessment selection process to the brain morphometric characteristics of each individual patient. We implement this paradigm using a newly developed learning-to-rank method PLTR . Our empirical study on the ADNI data yields promising results to identify and prioritize individual-specific cognitive biomarkers as well as cognitive assessment tasks based on the individual's structural MRI data. The resulting top ranked cognitive biomarkers and assessment tasks have the potential to aid personalized diagnosis and disease subtyping  .
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, determining a proper set of cognitive tests is a widely studied topic. Most existing studies aim to create selection guidelines for a targeted population but not 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 method PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments on 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 top ranked cognitive biomarkers have the potential to aid personalized diagnosis and disease subtyping , and to make progress towards enabling precision medicine in AD .
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We propose an innovative machine learning paradigm enabling precision medicine for prioritizing cognitive assessments according to their relevance to
Background: Cognitive assessments represent the most common clinical routine for the diagnosis of
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disease at the individual patient level. The paradigm tailors the cognitive biomarker discovery and cognitive assessment selection process to the brain morphometric characteristics of each individual patient. We implement this paradigm using
Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, determining a proper set of cognitive tests is a widely studied topic. Most existing studies aim to create selection guidelines for a targeted population but not for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method: We adapt
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to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments on top of the prioritization list. We also extend PLTR to better separate the most effective cognitive assessments and the less effective ones. Results:
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yields promising results to identify and prioritize
shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing
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as well as cognitive assessment tasks based on the individual's structural MRI data. The resulting
. 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
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and assessment tasks
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, and to make progress towards enabling precision medicine in AD