人工智能方法用于在老年人中早期检测神经认知障碍
Khalid AlHarkan1, Nahid Sultana2, Noura Al Mulhim3
1Department of Family and Community Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Frontiers in computational neuroscience
|March 6, 2024
概括
人工智能 (AI) 模型准确地预测沙特患者的认知衰退. 支持矢量机 (SVM) 模型的准确度超过93%,识别了早期痴呆症检测的关键预测因素,如教育和慢性疼痛.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 痴呆症是一个重大的全球健康挑战,其标志是渐进性的认知能力下降.
- 早期发现轻度认知障碍 (MCI) 和痴呆症对于有效管理至关重要.
研究的目的:
- 应用人工智能 (AI) 方法来准确检测MCI和痴呆症.
- 开发和评估人工智能模型,用于沙特人口的认知衰退预测.
主要方法:
- 这是一项涉及343名沙特患者的定量研究.
- 利用后勤回归和支持向量机 (SVM) 算法来检测认知衰退.
- 分析了认知功能与人口统计学/医疗因素之间的关联,使用了奇平方测试.
- 为男性和女性开发了单独的预测模型.
主要成果:
- 患病率:54%的正常认知,34%的MCI,12%的痴呆症.
- 人工智能模型显示预测准确度超过71%.
- SVM模型实现了最高的准确性:93.3% (所有患者),94.4% (男性) 和95.5% (女性).
- 通过SVM识别的关键预测因素包括教育,睡觉时间,慢性疼痛药物,糖尿病,中风,性别,慢性疼痛,冠状动脉疾病和起床时间.
结论:
- 拟议的AI方法为认知衰退预测提供了高准确度和可靠性.
- 这些工具可以帮助医疗保健从业人员在早期发现痴呆症.
- 研究结果为未来关于认知衰退和新兴趋势的研究提供了洞察力.
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