RMME/STAT Joint Colloquium

Machine Learning Prediction Modeling for Longitudinal Outcomes in Older Adults

Dr. Jaime Lynn Speiser
Wake Forest School of Medicine

Friday, December 10th, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=macf5fd1f3af4a057a735eeefe6e40af0

Prediction models aim to help medical providers, individuals and caretakers make informed, data-driven decisions about risk of developing poor health outcomes, such as fall injury or mobility limitation in older adults. Most models for outcomes in older adults use cross-sectional data, although leveraging repeated measurements of predictors and outcomes over time may result in higher prediction accuracy. This seminar talk will focus on longitudinal risk prediction models for mobility limitation in older adults using the Health, Aging, and Body Composition dataset with a novel machine learning method called Binary Mixed Model (BiMM) forest. I will give an overview of two common machine learning methods, decision tree and random forest, before introducing the BiMM forest method. I will then apply the BiMM forest method for developing prediction models for mobility limitation in older adults.

 

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