Combining artificial neural networks and a marginal structural model to predict the progression from depression to Alzheimer's disease.

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.