Tag: Saving Lives

News

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.

News

Welcoming Leoni Grossman!

We are happy to welcome another addition to the research group: 🥁🥁🥁 Leoni Grossman!

Leoni is a PhD student in John Wallert‘s team. She has a Masters in Biomedicine with a minor Neuroinformatics and another masters in Applied Computational Life Sciences.

The main scientific objective of Leoni’s PhD is to advance our knowledge in suicide prediction. Her is focused on statistical modelling in two different projects:

  1. Suicide and compulsory care – A registry study of risk factors for suicide among psychiatric patients under compulsive mental care.
  2. Project Saving Lives – Aiming to derive and validate better risk models for suicide in a nationwide suicide cohort using multimodal data.

🎺

News

Grant for suicide research

We have received $1,494,898 from the American Foundation for Suicide Prevention (AFSP) for the Saving Lives project!

The project aims to improve precision in prediction and prevention of suicide by constructing a national cohort of genetic and environmental data. The ultimate goal is to decrease suicide rates.

Suicide is a tragic event for the individual, the relatives and society. We therefore make efforts to prevent suicide, and a typical way of doing this is to assess suicide risk. Still, studies in psychiatric populations show that current assessments available will classify one out of two suicidal individuals as low risk before an actual death by suicide, while 95% of those classified as high risk will eventually not die by suicide.

Why?

The causes of suicide are complex. Many different factors and life events contribute. To improve prediction, large studies and long follow-up are needed to reach meaningful sample sizes.

Using unique Swedish assets, of genetic and environmental data, and machine learning we have an opportunity a discover environmental and genetic risk factors associated with suicide in order to predict and prevent it.

We thank the AFSP for this grant.

Read more about the Saving lives project here.

News

PhD position in Precision Psychiatry available

We are looking for a new doctoral student to join our computational team!

Read more and apply here.

Do you want to contribute to top quality medical research?

In the research group we work to improve the lives of individuals with psychiatric conditions. A core theme of our research is to develop more accurate prediction models for both the risk of, and also consequences of, psychiatric conditions.

The main scientific objective of your PhD project is to advance knowledge in suicide prediction. Suicide is a catastrophic event for the individual, close relatives, colleagues, and society. Your project is focused on statistical modelling in two different studies. In both studies you will collaborate with colleagues at Oxford University (UK), University of North Carolina Chapel Hill (US) and Sweden.

🎯Research project 1 is a registry study of risk factors for suicide among psychiatric patients that have experienced compulsive mental care.

🎯Research project 2 you will be working with multimodal data (including genetic data) in a nationwide suicide cohort with the purpose of improving present prediction of suicide aiming to derive and validate better risk models for suicide in the total population.

The position is a part of the Rücklab computational team, led by John Wallert.

We look forward to receiving your application!