Machine learning for vision based patient monitoring

用于基于视觉的患者监测的机器学习

• 批准号: 2416606
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2416606
• 关键词: Machine; learning; vision; based; patient

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

基于视觉的患者监测（VBM）目前包括一系列现有技术，如心脏和呼吸率[1]等生命体征的远程监测。它已经在许多临床环境中产生了积极的影响，例如精神卫生保健、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，并使患者获得更好的睡眠。更智能的视觉系统将开辟更广泛的有用的下游应用，并最终为临床医生提供更好的数据。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，专家之间经常会有主观的分歧。这项工作旨在表明，在分析睡眠时，使用VBM的机器学习方法可以达到与独立专家相似或更好的一致性水平。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：烦躁不安、自残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例子。传统上，机器学习成功的关键支柱之一是大型、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。我将探讨如何克服这些挑战，以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提供客观的睡眠测量。使用最先进的计算机视觉方法与现有的生物标志物数据相结合，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。拟议的研究与EPSRC的“人工智能技术”和“医学成像”领域高度一致。我的研究是由Oxehealth赞助的，这是一家基于视力的患者监测公司，于2012年从牛津生物医学工程研究所分拆出来M. Villarroel等人，“新生儿重症监护病房早产儿的非接触生理监测”，npj数字医学，第2卷，第2期。1、艺术。否。2019年12月1日，doi: 10.1038/s41746-019-0199-5.[2]H. Lloyd-Jukes， O. J. Gibson， T. Wrench， A. Odunlade和L. Tarassenko，“基于视觉的精神健康环境患者监测和管理”，《临床工程杂志》，第46卷，第46期。1, pp. 36-43, 2021年3月，doi:10.1097/JCE.0000000000000447.[3]H. Danker-Hopfe等人，“根据Rechtschaffen & Kales和新的aasm标准的睡眠评分的相互可靠性”，《睡眠研究杂志》，第18卷，第18期。1, pp. 74-84, 2009, doi: 10.1111/j.1365-2869.2008.00700.x