AIMC Topic: Mental Health

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Causal models adjusting for time-varying confounding-a systematic review of the literature.

International journal of epidemiology
BACKGROUND: Obtaining unbiased causal estimates from longitudinal observational data can be difficult due to exposure-affected time-varying confounding. The past decade has seen considerable development in methods for analysing such complex longitudi...

The impact of social housing on mental health: longitudinal analyses using marginal structural models and machine learning-generated weights.

International journal of epidemiology
BACKGROUND: Social housing may provide an affordable and secure residential environment, but has also been associated with stigma, poor housing conditions and locational disadvantage. We examined the cumulative effect of additional years, and tenure ...

Rise of the machines? Machine learning approaches and mental health: opportunities and challenges.

The British journal of psychiatry : the journal of mental science
Machine learning methods are being increasingly applied to physical healthcare. In this article we describe some of the potential benefits, challenges and limitations of this approach in a mental health context. We provide a number of examples where ...

A Novel Model for Predicting Rehospitalization Risk Incorporating Physical Function, Cognitive Status, and Psychosocial Support Using Natural Language Processing.

Medical care
BACKGROUND: With the increasing focus on reducing hospital readmissions in the United States, numerous readmissions risk prediction models have been proposed, mostly developed through analyses of structured data fields in electronic medical records a...