Prediction of Self Harm and Suicidal Behaviour
This project has been progressing for a number of years and has recently benefitted from the involvement of the Mathematics Department at UWA.
Currently the second lot of yearly data is being analysed that it is hoped will produce an algorithm to predict self-harm and suicidal behaviour. Currently the world ability to do this is very poor. Perth clinic has an outstanding database of high-quality psychological measures. In addition, they have established sophisticated monitoring and feedback systems. These features set Perth Clinic apart and presents a unique opportunity to investigate psychological risk factors as dynamic interdependent variables.
Existing clinical tools and statistical models for suicide and self-harm risk stratification are generally considered to be inaccurate and ineffective. Research in this field is now investigating machine learning and artificial intelligence as a means to model the vast and complex collection of suspected risk factors. It is also now clear that many of the typical psychological risk factors which are often treated as static variables will in fact vary significantly over as little as several hours and should be investigated as dynamic interdependent variables. Researchers from The University of Western Australia (UWA), in collaboration with Perth Clinic, have already produced several pioneering investigations into the dynamic trajectories and interdependent relationships between variables in this data using latent growth class analysis and cross-lagged panel analysis.
This current study will comprehensively reanalyse existing data using high dimensional machine learning methods while adopting a networked dynamical systems approach. Given the magnitude and quality of the data, a comprehensive re-analysis is proposed using multivariate methods from the field of machine learning to investigate the possibility of improved risk stratification.
So, the aim is to examine if we can predict those patients at risk, and offer them a targeted treatment.