We are happy to announce that Olly Kravchenko just joined the Rück research lab as our new PhD student!
Olly has a background in project management, product testing, and user experience research in tech start-ups. She also has a MSc in German and English Language & Literature (Kyiv National Linguistic University) and a MSc in Public Health Sciences (Stockholm University). She recently finished an internship at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, working on a genetically informed research project using data from the Swedish Twin Registry.
Here at the Rück Research Lab, Olly will mainly be working with the PRiSMED project, with John Wallert as main supervisor and Christian Rück as co-supervisor.
About the project
PRiSMED: Predicting health and socioeconomic outcomes in patients with common psychiatric disorders
Depression, social anxiety disorder, panic disorder, and obsessive-compulsive disorder are common mental disorders with a combined point prevalence of 15%. Almost 50% of diagnoses leading to sick-leave in Sweden are psychiatric and their share is increasing. Cognitive behavior therapy (CBT) is first-line treatment for these conditions. Yet, 30-60% of patients will not respond to CBT. Several predictors of CBT outcome have been proposed. Results are mixed and lack predictive acuity to guide clinical decisions. Prediction studies using larger samples and multimodal data (clinical, register, genetic) are urgently needed. Coupled with advanced modelling, we could augment precision psychiatry allowing for tailored intervention and cost-effective resource use.
The project main purpose is improved outcome prediction for common mental disorders in routine clinical care. We will build prediction models for both clinical CBT outcomes (e.g., remission) and long-term outcomes (e.g., poverty) using multimodal data on 5,000 genotyped and register-linked patients treated with CBT – the hitherto largest data collection of its kind.
The project has three aims. Aim 1 uses traditional statistical methods to identify group-level predictors for these outcomes. Aim 2 applies Machine Learning (ML) to identify which individual will experience these outcomes. Aim 3 will plan and initiate a trial comparing a developed ML algorithm versus clinicians at predicting remission in prospective CBT patients.