Semi-Autonomous and autonomous cerebral physiologic artifact management platforms

汽车保险公司承担着为涉及机动车相关伤害案件的客户支付费用的作用。因此，准确估计和预测客户/付款人受伤后的临床轨迹对保险公司来说是有意义的，因为它有助于预测潜在的年度保单支付，并为此类公司提供具有战略意义的投资能力。在轨迹预测方面，特定的损伤案例比其他案例具有更多的不确定性，其中急性神经损伤(即。中/重度创伤性脑损伤(TBI)是目前短期和长期结果和支持服务需求预测很差的典型情况。此外，这类人口还承担着相当大的短期和长期财务支持成本，在加拿大每年超过170亿加元。马尼托巴省公共保险公司(MPI；Crown Corporation)已经确定了在这一人群中建立客户轨迹模型的新方法的必要性。最近在急性神经损伤中的轨迹建模已经证明了增加急性时相(即。头2周)来自床边监测设备的大脑生理数据，MPI热衷于将其作为一种新的手段来探索，以潜在地改进有助于公司财务规划的此类模型。然而，这样的大脑生理数据流通常是大的、复杂的并且充满了人为误差，限制了它们在大多数轨迹模型中的包含。伪影误差主要表现为病人运动噪声、护理程序噪声和一般计算机连接误差。目前，数据构件管理需要手动清理，这种方法不利于客户结果建模的有效数据使用，也不是保险公司的可行选择。利用全球数据科学、生理学专业知识和现有数据来源，MPI将与泽勒博士合作，通过开发和验证用于脑生理学人工制品管理的自主计算机驱动解决方案来弥合现有的知识差距，这将有助于自动生成干净的数据，并将其纳入未来的客户轨迹建模，以帮助公司的财务规划。该项目将为2倍的硕士研究生和1倍的博士研究生，以及几个本科生提供培训。

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.