Scientists use machine learning technology to accurately identify depression and psychosis
Machine learning technology could be the secret to unlocking the complexity of mental illness, a major study has revealed.
The research, published by the University of Birmingham, is among the first to explore the use of technology to support the diagnosis of serious mental health illnesses, such as psychosis and depression.
According to the paper, patients with poor mental health rarely experience just one symptom in isolation, making it difficult to identify the “primary” condition and provide the proper treatment.
“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,” said the authors.
“Clinicians diagnosing psychosis, for example, would frequently regard depression as a secondary illness, with implications for treatment decisions which focus more on psychosis symptoms.”
To carry out the study, the researchers examined questionnaire responses, detailed clinical interviews and data from structural magnetic resonance imaging from a cohort of 300 patients who took part in a previous study.
Using the data, the team were able to build machine learning models of “pure” depression and “pure” psychosis and applied them to the group based on their reported symptoms. The results were then compared against the clinical diagnosis given by the patient’s doctor.
It was found that patients with depression as a primary illness were more likely to be diagnosed accurately, but patients with psychosis with depression had symptoms which “most frequently tended towards the depression dimension” – indicating that depression “plays a greater part in the illness than had previously been thought”.
Commenting on the study, lead author Paris Alexandros Lalousis said: “There is a pressing need for better treatments for psychosis and depression, conditions which constitute a major mental health challenge worldwide.
“Our study highlights the need for clinicians to understand better the complex neurobiology of these conditions, and the role of ‘co-morbid’ symptoms; in particular considering carefully the role that depression is playing in the illness.
“In this study we have shown how using sophisticated machine learning algorithms which take into account clinical, neurocognitive, and neurobiological factors can aid our understanding of the complexity of mental illness.”
He added: “In the future, we think machine learning could become a critical tool for accurate diagnosis. We have a real opportunity to develop data-driven diagnostic methods – this is an area in which mental health is keeping pace with physical health and it’s really important that we keep up that momentum.”
The latest figures suggest that around one in four people in the UK experience a mental health problem each year, with the most common illnesses being anxiety and depression.