Longitudinal trajectory identification of Post-Traumatic Stress Disorder using Machine Learning for high-dimensional cognitive, emotional and biological data

道路交通事故或袭击等造成的创伤影响到全世界数百万人，构成重大的健康负担。一些人在潜在的创伤事件后出现精神障碍的症状，而另一些人则没有。为了尽快提供有针对性的治疗服务，重要的是要在早期阶段就可靠地识别出那些患有精神障碍的高风险个体。以往对预后因素的研究有限，不同的研究往往发现不同的预测因素。这有两个方法上的原因。首先，当患者在精神障碍发病后很长一段时间进行回顾性检查时，很难明确区分精神疾病的危险因素和精神疾病的后果。其次，当利用广义线性模型应用传统统计方法来孤立地调查某些风险因素的集中假设时，很难检测到虚假关系（省略相关变量）或变量冗余。为了克服第一个问题，有必要在潜在的创伤事件发生后不久对人们进行检查，以确定未来精神病理的近端危险因素。为了解决第二个问题，有必要检查包含所有相关信息的高维数据，或者至少比以前检查的更多的观察结果，并使用统计方法，如机器学习，允许在一段时间内仔细检查这些高维数据。该项目的目的是通过将机器学习应用于纽约市贝尔维尤医院中心急诊室患者的高维数据，在潜在创伤事件发生后以及1、3、6和12个月后，最大限度地识别可预测的风险因素集。通过几个测量点的高维纵向数据的前瞻性收集将允许在12个月的过程中识别创伤后应激障碍（PTSD）的不同轨迹。由于在PTSD中，共病是一种规律而非例外，因此收集的高维数据集也将用于从遗传、生理、心理和社会学特征中识别共病的不同轨迹。因此，进一步的目标是预测这些特定轨迹的成员资格，并验证独立样本中的风险因素。综上所述，准确预测集的发现将能够在潜在创伤事件后的急性期识别属于不同精神病理学轨迹的人。这具有重要的公共卫生影响；它为有针对性的治疗选择提供新的战略信息，并优化有效和高效的治疗服务分配。

Traumatic injuries resulting e.g. from road traffic accidents or assaults affect millions of people worldwide and constitute a major health burden. Some individuals develop symptoms of mental disorders after potential traumatizing events while others do not. To offer targeted therapeutic services as soon as possible, it is important to reliably identify those individuals that are at high risk for developing mental disorders already at an early stage. Previous research on prognostic factors has been limited, with different studies often finding different predictors. There are two methodological reasons for this. First, when patients are examined retrospectively, long time after the onset of a mental disorder, it is difficult to clearly distinguish between risk factors for mental illness and the consequences of mental illness. Second, when conventional statistical methods are applied utilizing a Generalized Linear Model to investigate focused hypothesis on certain risk factors in isolation, spurious relationships (omitting relevant variables) or redundancy of variables are hard to detect. To overcome the first problem, it is necessary to examine people shortly after a potential traumatic event in order to identify proximal risk factors for future psychopathology. To solve the second problem, it is necessary to examine high-dimensional data that comprises all relevant information, or at least much more observations than previously examined, and to use statistical methods such as Machine Learning that allow to scrutinize such high-dimensional data over the course of time.The aim of the project is to identify maximally predictive sets of risk factors by applying Machine Learning on high-dimensional data from patients at the emergency room of Bellevue Hospital Center in New York City immediately after a potentially traumatic event as well as 1, 3, 6 and 12 months later. The prospective collection of high-dimensional longitudinal data across several measurement points will allow identifying different trajectories of posttraumatic-stress disorder (PTSD) over the course of 12 months. Since comorbidity is the rule rather than the exception in PTSD the collected high-dimensional dataset will also be used to identify different trajectories of comorbidity from genetic, physiological, psychological, and sociological characteristics. A further aim is therefore to predict membership to these specific trajectories and to validate the risk factors in an independent sample.Taken together, the discovery of accurate predictor sets will enable the identification of people during the acute phase after a potential traumatic event who belong to different trajectories of psychopathology. This has important public health implications; it informs new strategies for specifically targeted treatment selection and optimizes the effective and efficient allocation of treatment services.