机器学习利用病史、载脂蛋白E基因型和神经心理学评估预测从正常衰老到轻度认知障碍的转变。
Machine Learning Predicts Conversion from Normal Aging to Mild Cognitive Impairment Using Medical History, APOE Genotype, and Neuropsychological Assessment.
作者信息
Prabhakaran Divya, Grant Caroline, Pedraza Otto, Caselli Richard, Athreya Arjun P, Chandler Melanie
机构信息
Center for Individualized Medicine, Mayo Clinic, Jacksonville, FL, USA.
Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
出版信息
J Alzheimers Dis. 2024;98(1):83-94. doi: 10.3233/JAD-230556.
BACKGROUND
Identifying individuals at risk for mild cognitive impairment (MCI) is of urgent clinical need.
OBJECTIVE
This study aimed to determine whether machine learning approaches could harness longitudinal neuropsychology measures, medical data, and APOEɛ4 genotype to identify individuals at risk of MCI 1 to 2 years prior to diagnosis.
METHODS
Data from 676 individuals who participated in the 'APOE in the Predisposition to, Protection from and Prevention of Alzheimer's Disease' longitudinal study (N = 66 who converted to MCI) were utilized in supervised machine learning algorithms to predict conversion to MCI.
RESULTS
A random forest algorithm predicted conversion 1-2 years prior to diagnosis with 97% accuracy (p = 0.0026). The global minima (each individual's lowest score) of memory measures from the 'Rey Auditory Verbal Learning Test' and the 'Selective Reminding Test' were the strongest predictors.
CONCLUSIONS
This study demonstrates the feasibility of using machine learning to identify individuals likely to convert from normal cognition to MCI.
背景
识别轻度认知障碍（MCI）风险个体具有迫切的临床需求。
目的
本研究旨在确定机器学习方法能否利用纵向神经心理学测量、医学数据和APOEɛ4基因型来识别在诊断前1至2年有MCI风险的个体。
方法
来自676名参与“载脂蛋白E在阿尔茨海默病的易感性、保护和预防中的作用”纵向研究的个体的数据（N = 66名转化为MCI者）被用于监督机器学习算法中，以预测向MCI的转化。
结果
随机森林算法在诊断前1至2年预测转化的准确率为97%（p = 0.0026）。“雷伊听觉词语学习测验”和“选择性提醒测验”中记忆测量的全局最小值（每个个体的最低分数）是最强的预测因子。
结论
本研究证明了使用机器学习识别可能从正常认知转化为MCI个体的可行性。
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