Date of Award


Degree Type


Degree Name

Master of Science in Psychology


Behavioral Sciences



First Advisor

Theodore A. Walls


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.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.