Achieving Personalised Medicine in Mental Health through Use of Routine Clinical Patient Data
Summary
My work focusses on solving practical clinical problems in mental health through the use of readily available datasets through advanced data science techniques.
As a psychiatrist and a data scientist, I have been able to use the great wealth of clinical electronic patient records from mental health hospitals and secondary mental health Trusts in order to characterise the inflammatory and cardiometabolic landscape of severe and enduring mental illness, as well as to produce clinically useful risk prediction models.
Project aims
Applicants to this scheme will be allowed to create their own project within the area of digital psychiatry and risk prediction modelling, and will be supported to
a) come up with a clinically relevant question that the available datasets can answer;
b) train in bioinformatic, computational and statistical techniques needed to manage and analyse the data;
c) answer the questions and start the process of moving towards patient benefit.
The applicant will be able to work with several datasets, as needed:
- Large electronic health record datasets from UK-based NHS Trusts for secondary mental health;
- The emerging Early Psychosis Mission clinical, records, and ‘omic dataset;
- Large primary care (GP) record datasets;
- UK Biobank - comprising clinical, imaging, biochemical, genetic and other 'omic data for half a million UK residents;
- GTEx and other transcriptomic/genomic datasets from different tissues.
PhD students working under my supervision will be co-supervised by Prof Murray.
Contact details
Dr Emanuele Osimo - efo22@cam.ac.uk
Opportunities
This project is open to applicants who want to do a:
- PhD
Necessary attributes: being curious and interested in neuroscience - with an interest in using quantitative and computational research tools.
Ideal attributes: data science and/or bioinformatics skills. A psychology/neuroscience background.