Leoni’s ISP seminar

New PhD students are thriving at Rücklab right now! Today Leoni had her individual study plan (ISP) seminar, presenting her upcoming doctoral studies.

Leoni’s doctoral project goes under the title “Predictive modeling of suicide risk and risk factors using registry and genetic data” and is all about suicide prediction using new technologies and unique multimodal data.

A challenge in the current research field of suicide prevention is that it is hard to study such rare events. To date, the focus has been on suicidal thoughts or attempts rather than actual deaths, and there are reasons to believe that these events differ in terms of prediction. When it comes to compulsory care, we know little about how the current interventions does in the long run, and there is a need for improved suicide prediction tools that can be implemented in a clinical context. 

Leoni will work with the projects 1) Suicide and compulsory mental care and 2) Saving Lives.

  • The first project will be about risk factors for suicide among psychiatric patients under compulsory mental care and will describe and compare suicide risk for these patients, as well as identify risk factors.
  • The second project will be about improving suicide prediction in a total nationwide multimodal suicide cohort. This project aims at discovering genetic and environmental risk factors and also develop predictive models for suicide death.

Leoni’s main supervisor is John Wallert. Co-supervisors are Christian Rück and Ronnie Pingel.

Olly’s ISP seminar

Today, Olly held her individual study plan (ISP) seminar for her doctoral studies. The overall purpose of the studies is to enhance the prediction of health and socioeconomic outcomes in patients with common psychiatric disorders, such as depression and anxiety.

We know that 25-50% of patients with depression and anxiety disorders don’t respond to CBT. These patients may benefit from alternative or tailored treatment formats. We want to explore ways to help them, but in order to do that we need to predict who they are. Existing predictive models of treatment outcome are usually based on clinical data with a small set of predictors which results in low predictive power and limited clinical utility.

Olly’s PhD project will use a big sample from Psykiatri Sydväst’s Internet Psychiatry and predict treatment outcomes for multiple disorders. It will use clinical, genetic and register data to predict both treatment response and long-term socioeconomic disadvantage such as labour market marginalization.

The work consists of four studies.

  • Study 1: Clinical, socioeconomic and genetic predictors of treatment outcomes in internet-delivered cognitive behavioral therapy for depression and anxiety disorders
  • Study 2: Development of a machine learning model to predict response to ICBT treatment at the individual subject level
  • Study 3: Development of a machine learning model aimed at predicting labour market marginalization
  • Study 4: Validation of the model from study 2 in clinical practice

Olly’s main supervisor is Christian, and her co-supervisor is John.

We look forward to this!