Background We sought to develop machine learning (ML) models trained on administrative
data which predict risk of readmission in patients with heart failure and to evaluate and …

Objective The objective of this study is to characterise concurrent use of benzodiazepine
receptor modulators and opioids among prescription opioid users in Alberta in 2017. Design A …

Objectives Coprescribing of benzodiazepines/Z-drugs (BZDs) and opioids is a drug-use
pattern of considerable concern due to risk of adverse events. The objective of this study is to …

Importance Machine learning approaches can assist opioid stewardship by identifying high-risk
opioid prescribing for potential interventions. Objective To develop a machine learning …

Objective To develop machine learning models employing administrative health data that
can estimate risk of adverse outcomes within 30 days of an opioid dispensation for use by …

Background As part of a series of feasibility studies following the development of Canadian
vaccine barcode standards, we compared barcode scanning with manual methods for …

Objective To develop a machine-learning (ML) model using administrative data to estimate
risk of adverse outcomes within 30-days of a benzodiazepine (BZRA) dispensation in older …

Objective To construct a machine-learning (ML) model for health systems with organised
falls prevention programmes to identify older adults at risk for fall-related admissions. Design …

Background Type-2 diabetes rates in First Nations communities are 3-5 times higher than
the general Canadian population, resulting in a high burden of disease, complications and …

Challenges exist for the management of diabetes care in First Nations populations. RADAR (Reorganizing
the Approach to Diabetes through the Application of Registries) is a culturally …