Identifying spatial and temporal pain profiles to identify disease type and progression from the Manchester Digital Pain Manikin

通过曼彻斯特数字疼痛模型识别空间和时间疼痛特征​​，以识别疾病类型和进展

• 批准号: 2777116
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2777116
• 关键词: Identifying; spatial; temporal; pain; profiles

Chronic pain drives disability in people with musculoskeletal and other chronic conditions and affects approximately one in five people worldwide. Chronic pain leads to deterioration people's physical and mental health, which in turn causes disability that results in lower productivity, increased work absenteeism and impaired social functioning. Precise figures on pain prevalence are still largely unknown and further knowledge gaps exist with respect to what causes pain and how best to manage it. To address this, researchers need validated methods to measure pain in large, representative populations.Pain manikins, also known as pain maps or pain diagrams, e human body-shaped figures that -compared to text-based questionnaires-enable intuitive self-reporting of pain location by shading or selecting affected body areas [1]. We have developed the Manchester Digital Pain Manikin which enables people to quickly and intuitively self-report pain location and location-specific pain intensity on their smartphone [2]. Pain manikins are currently used to accurately calculate a patient's pain extent, i.e. what percentage of their body is affected by pain. However, the detailed data provided through the pain manikin means that it may be possible to extract additional information about a patients's condition and prognosis by analysing the spatial patterns (i.e. where the pain is located) and temporal patterns (i.e. how does pain change over time).These types of complex patterns may be identified using machine learning methods. Such methods have previously been shown to be effective in medical imaging applications [3]. In the first instance, you will have access to a data set collected as part of the Manchester Digital Pain Manikin feasibility study. For this, 108 people with a clinician diagnosis of rheumatoid arthritis, osteoarthritis or fibromyalgia will submit daily manikin reports for 30 days, alongside a single item asking them about their overall pain intensity for that day. They will also complete a more extensive pain questionnaire at baseline and again at the last day of follow-up.In this PhD project, you will:1. Gain insight in the current state of play of (machine learning) methods for analysing digital manikin data2. Develop skills to apply and create machine learning techniques for analysis of digital pain manikin data to identify disease types and trajectories3. Learn how to incorporate manikin-based analytics into a clinical decision support tool for diagnosis and/or monitoring.

慢性疼痛会导致肌肉骨骼和其他慢性病患者的残疾，并影响全球大约五分之一的人。慢性疼痛会导致人们的身心健康恶化，这又导致残疾，从而导致生产力降低，增加工作缺勤和社会功能受损。关于疼痛患病率的精确数字仍然是未知的，并且在导致疼痛以及如何最好地管理疼痛的原因方面存在进一步的知识差距。为了解决这个问题，研究人员需要经过验证的方法来测量大型代表性人群中的疼痛。媒介的手绘，也称为疼痛图或疼痛图，E人体形状的数字，与基于文本的问卷 - 可启用的疼痛位置的直觉自我报告相关，可以通过阴影或选择受影响的身体区域[1] [1]。我们已经开发了曼彻斯特的数字疼痛手感，使人们能够在智能手机上快速，直觉上的自我报告疼痛位置和特定于位置的疼痛强度[2]。目前使用疼痛手工蛋白来准确计算患者的疼痛程度，即受到疼痛影响多少百分比。但是，通过疼痛手持蛋白提供的详细数据意味着，可以通过分析空间模式（即疼痛所在的位置）和时间模式（即疼痛如何随时间变化）来提取有关患者状况和预后的其他信息。使用机器学习方法可以鉴定出这些类型的复杂模式。此类方法先前已被证明在医学成像应用中有效[3]。首先，您将可以访问作为曼彻斯特数字疼痛Manikin可行性研究的一部分收集的数据集。为此，有108名患有临床医生诊断为类风湿关节炎，骨关节炎或纤维肌痛的人将每天提交30天的Manikin Reports，同时还有一个项目询问他们当天的整体疼痛强度。他们还将在基线和随访的最后一天完成一份更广泛的疼痛问卷。在这个博士学位项目中，您将：1。在分析数字Manikin Data2的（机器学习）方法的当前游戏状态中获得洞察力。开发技能以应用和创建机器学习技术来分析数字疼痛Manikin数据以识别疾病类型和轨迹3。了解如何将基于MANIKIN的分析纳入诊断和/或监测的临床决策支持工具中。