Using machine learning methods to identify factors associated with depression and posttraumatic stress disorder (PTSD)


Duration: minimum duration 5 months (longer also possible)

Location: Amsterdam UMC, location AMC, dept. Psychiatry / Leiden University, Dept of Clinical Psychology

Background research project: Depression and posttraumatic stress disorder (PTSD) are common mental disorders, which also commonly co-occur. Many risk factors for these disorders overlap, yet the research is often fragmented per single disorder and furthermore mostly focusses on one or two explanatory domains. Machine learning techniques can help to identify patterns in complex and intercorrelated data associated with presence of these mental disorders.


In this project we will identify shared and specific factors associated with depression and PTSD. To achieve this, a machine learning approach will be used to distinguish between these mental disorders and their comorbid states, using numerous biopsychosocial factors previously associated with these disorders in a large population-based data-base. We will build machine learning models that can successfully distinguish presence of depression and PTSD and their comorbid states. Next, we will use interpretable machine learning methods to interpret these models. This will give insight in which biopsychosocial factors are most strongly associated with each disorder and with the comorbid combination of depression and PTSD.

Role and requirements:

You will design and develop the machine learning algorithms using R. We’re looking for a master student with good knowledge of statistics and programming experience in R and/or python. You are interested in mental health and machine learning methods and are highly motivated, accurate and independent. English or Dutch speaking are both possible. Part of the internship can be done remotely.

If you are interested, please send me an email with your CV and a short motivation (Laura Nawijn).  Feel free to contact me if you have any questions!

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