Prioritization of Cognitive Assessments in Alzheimer's Disease via Learning to Rank using Brain Morphometric Data
作者： Bo Peng , Xiaohui Yao , Shannon L. Risacher , Andrew J. Saykin , Li Shen
关键词: Machine learning 、 Biomarker discovery 、 Empirical research 、 MEDLINE 、 Relevance (information retrieval) 、 Process (engineering) 、 Artificial intelligence 、 Learning to rank 、 Cognition 、 Psychology 、 Precision medicine
摘要: We propose an innovative machine learning paradigm enabling precision medicine for prioritizing cognitive assessments according to their relevance Alzheimer's disease at the individual patient level. The tailors biomarker discovery and assessment selection process brain morphometric characteristics of each patient. implement this using a newly developed learning-to-rank method PLTR. Our empirical study on ADNI data yields promising results identify prioritize individual-specific biomarkers as well tasks based individual's structural MRI data. resulting top ranked have potential aid personalized diagnosis subtyping.
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