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Activity Number: 75 - Contributed Poster Presentations: Biometrics Section
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #313046
Title: Neural Networks for Clustered and Longitudinal Data Using Mixed Effects Models
Author(s): Francesca Mandel* and Ian Barnett
Companies: University of Pennsylvania and University of Pennsylvania
Keywords: longitudinal analysis; mixed models; mobile health; neural networks; prediction

Mobile health data affords new opportunities for predicting future health status by leveraging an individual’s behavioral history alongside data from similar patients. Methods that incorporate both individual-level and sample-level effects are critical to using this data to its full predictive capacity. Neural networks are powerful tools for prediction, but many assume input observations are independent even when they are clustered or correlated in some way, such as in longitudinal data. Generalized linear mixed models (GLMM) provide a flexible framework for modeling longitudinal data but have poor predictive power particularly when the data is highly nonlinear. We propose a generalized neural network mixed model (GNMM) that replaces the linear fixed effect in a GLMM with the output of a feed-forward neural network. The model simultaneously accounts for the correlation structure and complex nonlinear relationship between input variables and outcomes, and it utilizes the predictive power of neural networks. We apply this approach to predict depression and anxiety levels of schizophrenic patients using longitudinal data collected from passive smartphone sensor data.

Authors who are presenting talks have a * after their name.

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