International Meeting for Computational Methods for Mental Health

第一部分：这个项目是关于组织一个国际研讨会，讨论心理健康评估的计算工具。对于许多精神疾病，早期干预可以显着改善个体的生活质量。根据世界卫生组织的数据，在美国和加拿大，精神健康障碍是导致残疾的主要原因，占残疾和过早死亡的所有寿命的25%。出现在童年和青春期早期的精神疾病症状实际上是多年前开始的过程的后期阶段。因此，精神病学研究对识别和检测风险标志物（遗传，神经，行为和/或社会偏差）非常感兴趣，这些标志物表明在症状出现之前发生特定精神疾病的风险增加。这次会议将在塞浦路斯举行，由于文化问题，那里的心理健康问题仍未得到适当解决。这次会议将使美国专家和研究生与塞浦路斯大学的研究人员聚集在一起，在那里，参与精神病学和计算机科学的科学家们开始了新的努力来解决这个问题。来自两国的CS，ECE和医学专家的互补技能将使这一新的研究领域受益，并最终使美国和世界受益。 讨论将侧重于解决现有技术中的差距，并制定战略以尽量缩小这一差距。这包括确定已经取得的进展，同时认识到陷阱，并确定新的机会，心理健康监测使用视觉和传感器网络技术。第2部分：本项目是关于组织一个国际会议，将涉及创建计算算法和智能分布式系统，以帮助早期诊断的个人谁是在发展中国家的行为障碍的风险。先前的研究表明，用于识别风险个体的行为调查的两个关键领域是运动活动和情绪范围显示的异常，特别是面部。运动异常是基于这样的观察，即运动控制涉及与多巴胺相关的大脑回路，这也与行为障碍有关。许多不同的疾病都观察到情绪范围调节的中断，因此应该讨论面部表情的监测。迄今为止，运动和情绪范围的评估已经由观看和评价个人视频的专家完成。然而，这些专家？主观评级将行为状况的分析限制在仅窄范围的行为，仅对个体受试者的小群体起作用，并且既昂贵又依赖于观察者的特定专业知识。为了能够进行更广泛的人群筛查，需要实现自动化。拟议的会议重点关注使用视觉和传感器网络的自动化过程的开发。 捕获和量化专家专业知识的创新方法将与评估行为演变的指标一起沿着呈现。此外，新的计算工具，支持干预措施的有效性评价将进行讨论。

Part 1:This project is about the organization of an international symposium that deals with computational tools for mental health assessment. Early intervention can dramatically improve the individual's quality of life, for many psychiatric disorders. Accounting for 25% of all years of life lost to disability and premature mortality, mental health disorders are the leading cause of disability in the United States and Canada, according to the World Health Organization. Symptoms of mental illness which emerge in childhood and early adolescence are actually the later stages of a process which began years earlier. Hence, psychiatric research is immensely interested in identifying and detecting risk-markers (genetic, neural, behavioral, and/or social deviations) that indicate elevated risk for the development of specific mental illnesses prior to the onset of symptoms. This meeting will be organized at Cyprus where mental health issues are still not addressed appropriately due to cultural issues. The meeting will bring US experts and graduate students together with researchers from University of Cyprus, where new efforts are initiated to deal with the problem by scientists involved with psychiatry and computer science. The complementary skills of CS, ECE, and medical experts from the two countries will benefit this novel research area, and ultimately benefit the U.S and the world. The discussions will be focused on tackling the gap in the current state of the art and developing strategies to minimize that gap. This includes identifying the progress that has already been made while recognizing the pitfalls and determining new opportunities for mental health monitoring using vision and sensor network technologies.Part 2:This project is about the organization of an international meeting that will involve the creation of computational algorithms and intelligent distributed systems to assist with the early diagnosis of individuals who are at risk of developing behavioral disorders. Previous research has indicated that two critical areas of behavioral investigation for use in identifying at-risk individuals have been abnormalities in motor activities and emotional range displays, especially of the face. Motor abnormalities are based on the observation that motor control involves the circuits of the brain associated with dopamine, which are also implicated in behavioral disorders. Many different disorders share the observation of disruption in the emotional range regulation, so monitoring of facial expressions should be discussed.To date, assessments of motor and emotional range have been done by the experts who view and rate videos of an individual. However, these experts? subjective ratings limit the analysis of behavioral conditions to only a narrow range of behaviors, work only for small populations of individual subjects, and are both costly and dependent on the observer's particular expertise. In order to enable a wider population screening, automation is required. The proposed meeting is focused on the development of this automated process that uses vision and sensor networks. Innovative ways of capturing and quantifying the expertise of experts will be presented along with metrics for assessing the evolution of the behavior. In addition, new computational tools that support the evaluation of the effectiveness of interventions will be discussed.