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

慢性疼痛会导致肌肉骨骼和其他慢性疾病患者的残疾，全球约五分之一的人会受到慢性疼痛的影响。慢性疼痛导致人们的身心健康恶化，进而导致残疾，从而导致生产率降低、缺勤增加和社会功能受损。关于疼痛患病率的准确数字在很大程度上仍然是未知的，关于引起疼痛的原因和如何最好地管理疼痛，还有更多的知识空白。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量具有代表性的人群的疼痛。Pain人体模型，也被称为疼痛地图或疼痛图，是一种人体形状的图形，与基于文本的问卷相比，它能够通过阴影或选择受影响的身体区域来直观地自我报告疼痛位置[1]。我们开发了曼彻斯特数字疼痛人偶，它使人们能够快速、直观地在智能手机上自我报告疼痛位置和特定位置的疼痛强度[2]。疼痛人体模型目前被用来准确计算患者的疼痛程度，即他们身体的多少百分比受到疼痛的影响。然而，通过疼痛模型提供的详细数据意味着有可能通过分析空间模式(即疼痛所在的位置)和时间模式(即疼痛如何随时间变化)来提取关于患者状况和预后的附加信息。这些类型的复杂模式可以使用机器学习方法来识别。这种方法以前已经被证明在医学成像应用中是有效的[3]。首先，您将可以访问作为曼彻斯特数字疼痛人体可行性研究的一部分收集的数据集。为此，108名被临床诊断为类风湿性关节炎、骨关节炎或纤维肌痛的人将提交为期30天的每日人体报告，并附上一项询问他们当天总体疼痛强度的项目。他们还将在基线和最后一天完成更广泛的疼痛调查问卷。在这个博士项目中，你将：1.深入了解分析数字人体模型数据的(机器学习)方法的当前状态2。培养应用和创建机器学习技术的技能，用于分析数字疼痛人体模型数据，以识别疾病类型和轨迹3。了解如何将基于Manikin的分析整合到用于诊断和/或监控的临床决策支持工具中。

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.