Adverse drug events (ADEs) represent one of the most prevalent types of health-care-
related harm, and there is substantial room for improvement in the way that they are …

Adverse drug events (ADEs) are responsible for a significant number of hospital admissions
and fatalities. Machine learning models have been developed to assess individual patient …

Background Although homelessness and opioid overdose are major public health issues in
the US, evidence is limited as to whether homelessness is associated with an increased risk …

Background The number of opioid-involved overdose deaths in the United States remains a
national crisis. The HEALing Communities Study (HCS) will test whether Communities That …

Background Little is known about whether machine-learning algorithms developed to predict
opioid overdose using earlier years and from a single state will perform as well when …

Aim To test the effect of the duration of medication for opioid use disorder (MOUD) use
during pregnancy on maternal, perinatal and neonatal outcomes. Design Retrospective …

Nationwide population-based cohort provides a new opportunity to build an automated risk
prediction model based on individuals' history of health and healthcare beyond existing risk …

Background Substance misuse is a heterogeneous and complex set of behavioural
conditions that are highly prevalent in hospital settings and frequently co-occur. Few hospital …

Purpose The global health crisis represents an unprecedented opportunity for the
development of artificial intelligence (AI) solutions. This paper aims to integrate explainable …

Targeting of location-specific aid for the US opioid epidemic is difficult due to our inability to
accurately predict changes in opioid mortality across heterogeneous communities. AI-based …