Machine learning for vision based patient monitoring

研究背景简介包括潜在影响基于视觉的患者监测(VBM)目前包括一系列现有技术，如远程监测心脏和呼吸频率等生命体征[1]。它已经在许多临床环境中产生了积极的影响，如精神卫生保健、急性护理和辅助生活，使临床医生更好地了解患者的生理状态，使患者有更好的夜间睡眠[2]。更智能的视觉系统将开辟更广泛的有用的下游应用，并最终开发出更好地为临床医生提供信息的数据。目的和目的睡眠监测的“黄金标准”工具是多导睡眠图(PSG)。然而，PSG产生的数据需要训练有素的专家来解释。此外，专家之间经常存在主观上的分歧[3]。这项工作旨在表明，使用VBM的机器学习方法在分析睡眠时可以达到类似的一致程度，或者比独立专家达到的更好。这项工作还旨在确定表明潜在健康问题的患者活动。特别是：躁动、自我伤害和过度锻炼是可以通过VBM使用机器学习方法识别的三个活动示例。传统上，机器学习成功的关键支柱之一是大量的带注释的数据集。然而，在医疗保健领域，数据往往是稀缺的、未标记的和敏感的。我将探索如何克服这些挑战，开发可用于解决上述下游任务的机器学习系统。研究方法的新颖性睡眠分期/量化仍然是一个活跃的研究领域。目前还没有一个单一的过程可以声称提供了客观的睡眠衡量标准。将最先进的计算机视觉方法与现有的生物标记物数据相结合，是实现这一目标的一种新方法。与EPSRC的战略和研究领域保持一致建议的研究与EPSRC的人工智能技术和医学成像领域非常一致。任何公司或合作者参与我的研究都得到了OxeHealth的赞助，OxeHealth是2012年从牛津生物医学工程研究所剥离出来的一家基于视觉的患者监测公司。[1]M.Villarroel等人，《新生儿重症监护病房中早产儿的非接触式生理监测》，NPJ Digital Medicine，第2卷，第1号，第1章。第1期，2019年12月，DOI：10.1038/s41746-019-0199-5.[2]H.Lloyd-Jukes，O.J.Gibson，T.Writch，A.Odunlade，and L.Tarassenko，《心理健康设置中基于视觉的患者监控和管理》，《临床工程杂志》，第46卷，第1期，第36-43页，2021年3月，DOI：10.1097/JCE.0000000000000447.[3]H.Danker-Hopfe等，《根据Rechtschaffen&Kales和新的AASMStandard进行睡眠评分的Interrater可靠性》，睡眠研究杂志，第18卷，第1期，第74-84页，2009年，DOI：10.1111/j.1365-2869.2008.00700.x

Brief description of the context of the research including potential impactVision based patient monitoring (VBM) currently encompasses a range of existing technologies such as the remote monitoring of vital signs such as heart and breathing rate [1]. It has already had a positive impact in many clinical settings, such as mental health care, acute care and assisted living, giving clinicians a better understanding of a patient's physiological state and patients a better night's sleep [2]. More intelligent visual systems would open up a wider range of useful downstream applications and, ultimately, data which can better inform clinicians.Aims and ObjectivesThe "gold standard" tool for sleep monitoring is polysomnography (PSG). However, the data produced by PSG requires interpretation by a trained expert. Additionally, there can often be subjective disagreement between experts [3]. This work aims to show that a machine learning approach using VBM can achieve a similar level of agreement, or better, than independent experts achieve when analysing sleep. This work also aims to identify patient activity that is indicative of underlying health concerns. In particular: restlessness, self-harm and excessive exercise are three examples of activity that could be identified through VBM using a machine learning approach.One of the key pillars to machine learning success has traditionally been large, annotated datasets. However, in healthcare, data is often scarce, unlabelled and sensitive in nature. I will explore how these challenges can be overcome, to develop machine learning systems that can be used to tackle the downstream tasks detailed above.Novelty of the research methodologySleep staging/quantification remains an active area of research. No single process can currently claim to provide an objective measure of sleep. Using state-of-the-art computer vision methods in combination with existing biomarker data, is a novel approach to achieve this.Alignment to EPSRC's strategies and research areasThe proposed research strongly aligns with the EPSRC areas of 'Artificial intelligence technologies' and 'Medical imaging'.Any companies or collaborators involvedMy research is kindly sponsored by Oxehealth, a vision based patient monitoring company spun out of the Oxford Institute of Biomedical Engineering in 2012.[1] M. Villarroel et al., 'Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit', npj Digital Medicine, vol. 2, no. 1, Art. no. 1, Dec. 2019, doi: 10.1038/s41746-019-0199-5.[2] H. Lloyd-Jukes, O. J. Gibson, T. Wrench, A. Odunlade, and L. Tarassenko, 'Vision-Based Patient Monitoring and Management in Mental Health Settings', Journal of Clinical Engineering, vol. 46, no. 1, pp. 36-43, Mar. 2021, doi:10.1097/JCE.0000000000000447.[3] H. Danker-Hopfe et al., 'Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASMstandard', Journal of Sleep Research, vol. 18, no. 1, pp. 74-84, 2009, doi: 10.1111/j.1365-2869.2008.00700.x