Other Interdiciplinary Incentives
Equipment & Techniques
I’m Emanuele, an Academic Clinical Lecturer in psychiatry in Cambridge. My research to date has aimed at characterising the immune and cardio-metabolic landscape of early psychosis and of schizophrenia using clinical research methods (e.g., MRI and biomarkers), as well as data science methods (e.g., electronic-health-record and cohort data). Together with my clinical experience as a psychiatrist, my data science and clinical informatics background have led me towards risk prediction modelling. Today, my research is mostly computational and aims to uncover the correlates and predictors for common clinical outcomes, both psychiatric (e.g., psychiatric clinical course and treatment responsiveness), and cardiometabolic (e.g., metabolic syndrome/diabetes) in people with serious mental illness, such as schizophrenia, depression and bipolar disorder.
MOZART: a risk prediction tool for treatment resistant schizophrenia
Commonly recorded clinical information at psychosis onset including blood markers can help predict whether a person will develop treatment resistant schizophrenia.