Suicide is a leading cause of death. A substantial proportion of the people who die by
suicide come into contact with the health care system in the year before their death. This …

Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine
learning-based early warning systems may reduce the time to recognition, but few systems …

A need exists to accurately estimate overdose risk and improve understanding of how to
deliver treatments and interventions in people with opioid use disorder in a way that reduces …

As the use of machine learning in high impact domains becomes widespread, the
importance of evaluating safety has increased. An important aspect of this is evaluating how …

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was
developed to promote the conduct of, and provide guidance for, predictive analyses of …

Importance Clinical trials have identified numerous prescriptive predictors of mental disorder
treatment response, ie, predictors of which treatments are best for which patients. However …

Abstract “Heterogeneous treatment effects” is a term which refers to conditional average
treatment effects (ie, CATEs) that vary across population subgroups. Epidemiologists are …

When randomized trials are run in a marketplace equilibriated by prices, interference arises.
To understand the impact on RCT analysis, we build a stochastic model of treatment effects …

Clinicians know that individual patients may respond differently to a given treatment and that
the overall treatment effect reported in a randomized trial of the treatment may not be directly …

It is commonfor treatments to yield different outcomes in different patients. Ifpatientcharacteristicsthatpredicttreatmentr…
be identified, understanding this heterogeneity of treatment effect (HTE) …