机器学习用于使用听觉和人口因素预测认知缺陷
Christopher E Niemczak1,2, Basile Montagnese1, Joshua Levy3,4,5,6
1Geisel School of Medicine at Dartmouth, Space Medicine Innovations Laboratory, Lebanon, NH, United States of America.
PloS one
|May 14, 2024
概括
审计评估可以预测艾滋病毒感染者的神经认知缺陷 (PLWH). 使用听觉数据的机器学习模型显著提高了预测准确度,与仅仅人口因素相比,为早期干预提供了潜力.
科学领域:
- 神经科学是一个神经科学.
- 传染性疾病 传染性疾病
- 机器学习 机器学习
背景情况:
- 认知功能障碍是艾滋病毒感染者 (PLWH) 的一个问题,特别是在资源较低的环境中.
- 早期发现认知障碍对于及时干预至关重要.
- 对神经认知缺陷的听觉评估的预测价值,除了人口统计数据,还需要进一步调查.
研究的目的:
- 利用机器学习来预测神经认知缺陷.
- 评估听力测试和人口因素在预测认知障碍方面的有效性.
主要方法:
- 在坦桑尼亚达累斯萨拉姆进行了一项纵向研究,涉及478名参与者 (349名PLWH,129名HIV阴性).
- 使用听觉变量和人口统计数据训练和测试机器学习算法.
- 神经认知功能障碍被定义为在Kiswahili蒙特利尔认知评估中得分<26.
主要成果:
- 结合听觉变量的机器学习模型证明了神经认知缺陷的优异预测.
- 斯和内核原始贝叶斯分类器显示出高预测性能,AUC值分别为0.91和0.87.
- 没有听觉特征的模型表现明显差 (p < .001),突出了听觉评估的增量预测价值.
结论:
- 听觉变量显著提高认知功能的预测.
- 易于管理的听觉测试可以作为神经认知表现的客观预测指标,适合全球应用.
- 进一步研究用于预测认知结果的机器学习算法是有必要的.
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