Predicting falls-related admissions in older adults in Alberta, Canada: a machine-learning falls prevention tool developed using population administrative health data.

ML prediction models based on population-level administrative data can assist health systems with fall prevention programmes identify older adults at risk of fall-related admissions and reduce costs. ML predictions based on ranked predictions or probability thresholds could guide subsequent interventions to mitigate fall risks. Increased access to diverse forms of data could improve ML performance and further reduce costs.