A way of using machine learning to more accurately identify patients with a mix of psychotic and depressive symptoms has been developed by researchers at the University of Birmingham.
Patients with depression or psychosis rarely experience symptoms of purely one or the other illness. Historically, this has meant that mental health clinicians give a diagnosis of a ‘primary’ illness, but with secondary symptoms. Making an accurate diagnosis is a big challenge for clinicians and diagnoses often do not accurately reflect the complexity of individual experience or indeed neurobiology.
Clinicians diagnosing psychosis, for example, would frequently regard depression as a secondary illness, with implications for treatment decisions which focus more on psychosis symptoms (e.g. hallucinations or delusions).
A team at the University of Birmingham’s Institute for Mental Health and Centre for Human Brain Health, working with researchers from the PRONIA consortium wanted to explore the possibility of using machine learning to create highly accurate models of ‘pure’ forms of both illnesses and to use these to investigate the diagnostic accuracy of a cohort of patients with mixed symptoms. Their results are published in Schizophrenia Bulletin.
The majority of patients have co-morbidities, so people with psychosis also have depressive symptoms and vice versa. That presents a big challenge for clinicians in terms of diagnosing and then delivering treatments that are designed for patients without co-morbidity. It’s not that patients are misdiagnosed, but the current diagnostic categories we have do not accurately reflect the clinical and neurobiological reality”.
Paris Alexandros Lalousis, Lead Author
The researchers examined questionnaire responses, detailed clinical interviews and data from structural magnetic resonance imaging from a cohort of 300 patients taking part in the PRONIA study, a European Union-funded cohort study taking place across seven European research centres.
