Systems Science, Data Science, and Machine Learning to Model the Dynamic Suicide Process
Date
2025-03-31
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
0000-0002-0934-6644
Type
Thesis
Degree Level
Doctoral
Abstract
Suicide and related behaviors, such as ideation, planning, and attempts, pose significant public health challenges globally. Accurately predicting these behaviors, which involve both observable (e.g., lethal attempts) and latent (e.g., suicidal ideation) aspects, is crucial for effective interventions. However, traditional models struggle to capture the complex dynamics of suicide-related behaviors, especially latent states.
This dissertation begins with a systematic scoping review of existing Systems Science models for suicide-related behaviors, identifying gaps in latent state modeling and the use of stochastic methods. These gaps inform the development of three distinct modeling approaches: regular System Dynamics (SD) modeling, SD enhanced with particle filtering, and SD incorporating Particle Markov Chain Monte Carlo (PMCMC).
Using time-series data on suicide-related deaths stratified by sex and method, the study develops an aggregated SD model followed by sex-stratified and sex-method-stratified models. To address the limitations of deterministic models, particle filtering and PMCMC are applied, introducing stochastic elements that improve the estimation of system states and underlying parameters, such as transition rates between suicidal behavior stages. PMCMC, in particular, refines parameter estimates, enhancing prediction accuracy.
The performance of these models is rigorously evaluated using metrics like root mean square error (RMSE), acceptance ratio of MCMC iterations, and the plot of observed and estimated data, wherever it was available. Results show that stochastic models, especially those incorporating PMCMC, outperform regular SD models in estimating both observed and latent behaviors. This research advances Systems Science methodologies in public health by demonstrating the value of stochastic methods in dynamic models.
Description
Keywords
suicide, systems science, system dynamics, data science, machine learning, canada
Citation
Degree
Doctor of Philosophy (Ph.D.)
Department
Computer Science
Program
Computer Science