Prioritization of Cognitive Assessments in Alzheimer's Disease via Learning to Rank using Brain Morphometric Data.

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 Epub Date: 2019-09-12 DOI:10.1109/BHI.2019.8834618

Bo Peng, Xiaohui Yao, Shannon L Risacher, Andrew J Saykin, Li Shen, Xia Ning

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引用次数: 2

Abstract

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.

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通过使用脑形态测量数据学习排序来确定阿尔茨海默病认知评估的优先级。

我们提出了一种创新的机器学习范式，使精准医学能够根据个体患者水平上与阿尔茨海默病的相关性来优先考虑认知评估。该范式根据每个个体患者的大脑形态特征定制认知生物标志物发现和认知评估选择过程。我们使用一种新开发的学习排序方法PLTR来实现这种范式。我们对ADNI数据的实证研究在识别和优先考虑个体特异性认知生物标志物以及基于个体结构MRI数据的认知评估任务方面取得了有希望的结果。由此产生的排名靠前的认知生物标志物和评估任务有可能帮助个性化诊断和疾病亚型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics

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