Orimaye Sylvester O, Schmidtke Kelly A
College of Global Population Health, University of Health Sciences and Pharmacy, St. Louis, MO, United States.
College of Arts and Sciences, University of Health Sciences and Pharmacy, St. Louis, MO, United States.
Front Dement. 2024 Apr 5;3:1362230. doi: 10.3389/frdem.2024.1362230. eCollection 2024.
Decades of research in population health have established depression as a likely precursor to Alzheimer's disease. A combination of causal estimates and machine learning methods in artificial intelligence could identify internal and external mediating mechanisms that contribute to the likelihood of progression from depression to Alzheimer's disease.
We developed an integrated predictive model, combining the marginal structural model and an artificial intelligence predictive model, distinguishing between patients likely to progress from depressive states to Alzheimer's disease better than each model alone.
The integrated predictive model achieved substantial clinical relevance when using the area under the curve measure. It performed better than the traditional statistical method or a single artificial intelligence method alone.
The integrated predictive model could form a part of a clinical screening tool that identifies patients who are likely to progress from depression to Alzheimer's disease for early behavioral health interventions. Given the high costs of treating Alzheimer's disease, our model could serve as a cost-effective intervention for the early detection of depression before it progresses to Alzheimer's disease.
数十年来对人群健康的研究已将抑郁症确定为阿尔茨海默病的可能先兆。人工智能中的因果估计和机器学习方法相结合，可以识别出有助于从抑郁症发展为阿尔茨海默病可能性的内部和外部中介机制。
我们开发了一种综合预测模型，将边际结构模型和人工智能预测模型相结合，比单独的每个模型更能区分可能从抑郁状态发展为阿尔茨海默病的患者。
当使用曲线下面积测量时，综合预测模型具有显著的临床相关性。它的表现优于传统统计方法或单独的单一人工智能方法。
综合预测模型可以成为临床筛查工具的一部分，该工具可识别可能从抑郁症发展为阿尔茨海默病的患者，以便进行早期行为健康干预。鉴于治疗阿尔茨海默病的高昂成本，我们的模型可以作为一种具有成本效益的干预措施，用于在抑郁症发展为阿尔茨海默病之前进行早期检测。
