Semi-Autonomous and autonomous cerebral physiologic artifact management platforms

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Auto-insurers carry the role of disbursement for clients involved in motor vehicle related injury cases. As such, accurate estimation and prediction of client/rate-payer clinical trajectory post-injury is of interest to insurers, as it facilitates forecasting potential annual policy disbursements and provides the ability for such corporations to be strategic with investments. Specific injury cases carry more uncertainty than others in trajectory prediction, with acute neural injury (ie. moderate/severe traumatic brain injury (TBI)) being the exemplar situation where current short- and long-term outcome and support services requirement predication is poor. Furthermore, such populations carry substantial short- and long-term financial support costs, which are over 17 billion CAD annually in Canada. Manitoba Public Insurance (MPI; Crown Corporation) has identified the need for novel approaches to client trajectory modelling within this population. Recent trajectory modelling in acute neural injury has demonstrated the benefit of adding acute-phase (ie. first 2 weeks) brain physiology data from bedside monitoring devices, and MPI is keen to explore this as a novel means to potentially improve such models that will aid with corporate financial planning. However, such cerebral physiologic data streams are often large, complex and rife with artifactual errors, limiting their inclusion in most trajectory models. Artifact errors take the form of patient motion noise, nursing procedural noise and general computer connection errors. Currently, data artifact management requires manual cleaning, with such methods not conducive to efficient data usage for client outcome modelling, nor a viable option for an insurer. Leveraging the global data science, physiologic expertise and existing data sources, MPI will work with Dr. Zeiler to bridge the existing knowledge gap through the development and validation of autonomous computer-driven solutions for cerebral physiology artifact management, that would facilitate automated generation of clean data to be incorporated into future client trajectory modelling aiding with financial planning of the corporation. This project will provide training for 2x MSc and 1x PhD graduate students, and several undergraduate students.