Continuous-time model identification: application on a behavioural (miLife) study

To develop efficient just-in-time personalised treatments, dynamical models are needed that provide a description of how an individual responds to treatment. However, available system identification approaches cannot effectively be applied to most behavioural datasets since, usually, the data collected is subjected to a large amount of noise and time sampling is not uniform. To be able to circumvent these issues, in this paper a new method is proposed for parsimonious system identification of continuous-time systems that does not require specially structured data. The developed algorithm provides an effective way to leverage these 'non-standard' datasets to identify continuous time dynamical models that are compatible with a-priori information available on the process. The algorithm developed is tested on data obtained from a behavioural study on adolescents and violence. The objective is to model the temporal dynamics of the association between violence exposure and mental health symptoms (depression and anxiety) in day-to-day life among a sample of adolescents at heightened risk for both substance use exposure and problem behaviour. The information extracted from individual models of behaviour such as the maximum burden and the time of fading away of depression/anxiety does differ substantially from person to person. This information has the potential to be useful to design personalised interventions that would have a better chance of succeeding.