Holly Fraser discusses new findings on whether and how we can predict antidepressant response using artificial intelligence.
[read the full story...]Predicting antidepressant response using artificial intelligence
No Responses »
Genetic risk for schizophrenia is associated with changes in heart structure and function
Nadine Parker and Ole Andreassen summarise a recent UK population-based cohort study, which looks at the impact of polygenic risk for schizophrenia on cardiac structure and function in over 32,000 people.
[read the full story...]Stratified care versus stepped care for depression: which is more effective?
Sarah Watts reviews a cluster randomised clinical trial investigating the effectiveness of stratified care compared to stepped care for depression, which has implications for IAPT services.
[read the full story...]Mental health stigma and online social support for bipolar disorder: what can we learn from Twitter?
Charlotte Walker explores an online ethnography study that explores how Twitter users discuss mental illness, particularly bipolar disorder, and in what context; focusing specifically on the areas of stigma and social support.
[read the full story...]Predicting suicide attempts in adolescents: machine learning is powerful, but don’t forget Bayes’ rule
Derek de Beurs explores a recent study that uses longitudinal clinical data and machine learning to predict suicide attempts in adolescents.
[read the full story...]Can a machine learning approach help us predict what specific treatments work best for individuals with depression?
Marcus Munafo explores a recent study that uses a machine learning approach across two trials (STARD*D and CO-MED) to try and predict treatment outcomes (primarily focusing on the antidepressant citalopram) for depression.
[read the full story...]