Fall risk prediction and validation in older adults: Leveraging electronic health records with machine learning

Falls in older adults are common and comorbid. Many fall risk stratification tools exist but they are often time-consuming and have limited accuracy. The goal of this thesis is to leverage machine learning and EHR data to create tools that can reliably estimate an individualized fall risk and provide a sound basis for decision-making regarding interventions to reduce fall risk.