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。对于许多精神疾病来说，早期干预可以极大地提高个人的生活质量。根据世界卫生组织的数据，在美国和加拿大，心理健康障碍占因残疾和过早死亡而失去的所有寿命的25%，是导致残疾的主要原因。精神疾病的症状出现在童年和青春期早期，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记物(遗传、神经、行为和/或社会偏差)非常感兴趣，这些标记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，在那里，由于文化问题，精神健康问题仍然没有得到适当的解决。这次会议将召集美国专家和研究生以及塞浦路斯大学的研究人员，在那里，精神病学和计算机科学的科学家们将发起新的努力来解决这个问题。CS、欧洲经委会和两国医学专家的互补技能将使这一新的研究领域受益，并最终造福于美国和世界。讨论的重点将是解决当前技术水平的差距，并制定将差距缩小的战略。这包括确定已经取得的进展，同时认识到陷阱，并确定使用视觉和传感器网络技术进行精神健康监测的新机会。第2部分：该项目是关于组织一次国际会议，其中将涉及创建计算算法和智能分布式系统，以帮助对有行为障碍风险的个人进行早期诊断。先前的研究表明，用于识别高危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，尤其是面部。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，多巴胺也与行为障碍有关。许多不同的障碍都可以观察到情绪范围调节的中断，所以应该讨论面部表情的监控。到目前为止，观看和评估个人视频的专家已经完成了对运动和情绪范围的评估。然而，这些专家呢？主观评级将行为条件的分析限制在很小的行为范围内，只适用于少数个体受试者，而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口筛查，需要自动化。拟议会议的重点是开发这一使用视觉和传感器网络的自动化过程。将介绍获取和量化专家专业知识的创新方法，以及评估行为演变的指标。此外，还将讨论支持评估干预措施有效性的新计算工具。

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.