LONGITUDINAL DATA PREDICTION IN EHR: COMPARISON OF GLMM AND MACHINE LEARNING METHODS

In this study, we aimed to develop and compare models to predict individuals with suicidal ideation using Generalized Linear Mixed Model (GLMM) and Machine Learning (ML) algorithms. We conducted secondary data analysis with data collected by an online clinical measurement company. The sample included 402 individuals aged over 18 years who have received more than three psychiatric treatments since 2017. The data were split into a training set (70%) and a testing set (30%) randomly. In the training set, GLMM, RF model, and GBDT model were trained with all the features. Conditional RF and GBDT with variables selected based on GLMM were trained next. Subsequently, the fitted models were used to predict suicide ideation in the test set. All analyses were conducted in R and Python. The prediction models based on ML algorithms (R2 from 0.260 to 0.409, MSE from 1.761 to 2.202, MAE from 0.942 to 0.985) performed better than GLMM (R2 = 0:115, MSE=2.880, MAE=1.013). The insights gained from this study may be of assistance to broadly apply ML algorithms to the massive data from EHR to enhance suicide risk prediction. There is, therefore, a definite need for improvements understanding prediction accuracy versus traditionally employed GLMM approaches