London: Researchers have developed a new Machine Learning (ML) technique to more accurately identify patients with a mix of psychotic and depressive symptoms.
While patients with depression as a primary illness are more likely to be diagnosed accurately, patients with depression and psychosis rarely experience symptoms of purely one or the other illness.
Those with psychosis with depression have symptoms which most frequently tend towards the depression dimension.
Historically, this has meant that mental health clinicians give a diagnosis of a ‘primary’ illness, but with secondary symptoms.
“The majority of patients have comorbidities, so people with psychosis also have depressive symptoms and vice versa,” said lead author Paris Alexandros Lalousis from the University of Birmingham in the UK.
“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,” Lalousis added.
For the study, published in the journal Schizophrenia Bulletin, the…