Researchers from the University of Birmingham are calling for a personalised psychosis treatment approach for patients.
The new paper, published in Translational Psychiatry, suggests that a ‘one size fits all’ approach may not be addressing the needs of all young people requiring early psychosis treatment. The researchers argued that Machine Learning techniques could be used to deliver bespoke psychosis treatment plans specifically target groups or individuals.
Psychosis is when people lose some contact with reality. The two main symptoms are hallucinations, which cause a person to hear, see and feel, smell or taste things that do not exist outside their mind and delusions, which cause a person to have strong beliefs that are not shared by others. Psychosis treatment typically involves a combination of antipsychotic medicine, psychological therapies, and social support.
Early intervention in Psychosis Services
Early intervention in Psychosis Services was first established in the 1990s and became recognised as offering the best chance of recovery for young people with the first episode of psychosis.
However, this gold standard approach may not be an effective psychosis treatment for all patients. The researchers suggest that a personalised approach could lead to greater precision in designing psychosis treatment plans, and also a better success rate in identifying patients who are on the wrong treatment path.
Lead researcher, Dr Lowri Griffiths, who was invited by the Editor-In-Chief of Translational Psychiatry to contribute to this review, said: “It is well known that early intervention leads to better outcomes, particularly among young people. However, despite receiving gold-standard treatment, a significant number of people are not benefitting from these interventions.
“We need to consider a range of factors from psychological, biological, and social circumstances to find the right treatments, for the right people, at the right time, to maximise a young person’s life chances. But in the first instance, this requires doing more to reach out to diverse and representative groups to ensure care is equitable for all”.
Machine Learning approach for psychosis treatment
A Machine Learning approach could act as a ‘guide’ for clinical decision-making, identifying with increased accuracy the key markers in patient data that would indicate the likely success or failure of any particular pathway.
This approach to psychosis treatment would help ensure that more patients could access personalised treatments that would likely benefit them, regardless of environment and social circumstances which might otherwise lead to inequality in healthcare.
Co-lead author Dr Paris Lalousis said: “The technology needed to devise treatment plans for individual patients, or groups of patients, already exists. We see machine learning already in use in several clinical areas, such as predicting responses to cancer treatment or identifying individuals at risk of needing intensive care. What we need is a framework that will enable us to investigate and test these technologies so we can harness them to improve outcomes for patients with psychosis.”
