Precision medicine is an emerging approach that focuses on identifying factors that are effective for disease treatment/prevention, at an individual level. Broadly, these approaches integrate clinical records, individual proteo/genomics, brain imaging or combinations therein, that transcend traditional clinical diagnostic groups for prognostic, and treatment response predictions. The past few years have seen a rapid response in the use of such methods in psychiatry research across the lifespan. Statistical models - data driven and model-based approaches - are being developed across data types to highlight the possibility of machine learning used throughout the care pathway, from primary to tertiary care level. These approaches are coming closer to real world translation and clinical implementation, however, face significant challenges. These include the standards and statistical models, reproducibility, and practical implementation in real world settings. The aim of this session is to showcase a few of the recent advances in the field of precision medicine in psychiatry, drawing from the recent BJPsych themed issue on this very topic. We will discuss the current challenges and also provide evidence pertaining to recovery outcomes and treatment response in psychosis and mood disorders. This session will enable clinicians to become familiar with the field that is increasingly presented in the research literature.
Chair: Professor Rachel Upthegrove, University of Birmingham and Lead Editor for the BJPsych Special Issue
What does the non-expert need to know when reading a data science paper? Five points to consider when reading a translational machine learning paper - Dr Rajeev Krishnadas, University of Glasgow and Guest Editor for the BJPsych Special Issue
Highlights of the BJPsych Special Issue
Dr Natalie Lane
