Predicting Fall Risks in Older Adults with Depression: A Machine Learning Approach - Ep 183 episode artwork

EPISODE · May 19, 2026 · 30 MIN

Predicting Fall Risks in Older Adults with Depression: A Machine Learning Approach - Ep 183

from ACCP JOURNALS · host ACCP JOURNALS

Dr. Ryan Carnahan, Pharmacotherapy's statistical scientific editor, interviews Dr. Jenny Lo-Ciganic about her research on the use of machine learning models to predict fall-related injuries among older adults with depression. Lo-Ciganic describes her work using real-world data and advanced analytics to improve medication safety and decision support. The discussion reviews the burden of falls and how depression increases risk, noting prior antidepressant–fall associations may reflect confounding by indication. They also address key injurious fall predictors, including frailty, age, prior falls, osteoarthritis, antidepressant dose, and regional social/health measures. Read the full manuscript at: https://accpjournals.onlinelibrary.wiley.com/doi/ftr/10.1002/phar.70087.

Episode metadata supplied by the publisher feed · Published May 19, 2026

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Predicting Fall Risks in Older Adults with Depression: A Machine Learning Approach - Ep 183

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