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

• Applicant: Professorin Dr. Katharina Schultebraucks
• Subject Area: Personality Psychology, Clinical and Medical Psychology, Methodology ; General, Cognitive and Mathematical Psychology ; Biological Psychiatry
• Term: from 2017 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 387444691

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.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Dr. Charles Marmar