Is Machine Learning Capable of Treating Mental Illness?
Recent developments in AI, has prompted further research into how machine learning (ML) can help with the identification, evaluation, and treatment of mental health issues.
The increasing rates of mental illness and the need for appropriate mental health care, coupled with recent developments in AI, has prompted further research into how machine learning (ML) can help with the identification, evaluation, and treatment of mental health issues. ML techniques can open up new pathways for the learning of behavioral patterns; the identification of such problems and psychiatric disorders; the production of disease progression observations; and personalization and optimization of therapies.
Despite the potential benefits of using machine learning in mental health, this is a new field of study, and designing effective ML-enabled applications that can be applied in practice is fraught with several complicated, interconnected challenges.
For instance, according to The Economic Times, “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 team found out that patients with depression as a primary disorder were more likely to be diagnosed correctly, but patients with psychosis with depression had symptoms that skewed to the depression factor the most often. This may mean that depression plays a bigger role in the disorder than previously believed.
It also mentioned, lead author Paris Alexandros Lalousis further said that “In the future, we think machine learning could become a critical tool for an 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.”
Advantages of Using Artificial Intelligence to Help Address the Mental Health Problem:
Provide assistance to medical practitioners
Like many sectors, AI can support practitioners in mental health services. Algorithms can interpret data much more quickly than humans, prescribe therapies, monitor client condition and warn people to any problems. AI and a human clinician can interact in several instances.
Available at all times of the day and night
It can take months to get an appointment due to a shortage of human mental health providers. Patients who live in places where there aren’t enough mental health providers who will have to wait forever. AI offers a service that a person can use at any time, without having to wait for an appointment.
As per report of Managed Healthcare Executive, here are two ways that machine learning is helping to change the face of mental health:
- Identifying biomarkers
One way that researchers are currently using machine learning is to help identify biomarkers, or specific representative biological measure, for specific conditions-or biomarkers that will help stratify patient populations.
Mental health disorders tend to be categorized quite broadly, and the symptoms of one person with a diagnosis of depression may be quite different from another person diagnosed with the same disease. And today, most psychiatrists must go through a trial-and-error approach to determine the right medication in the right dosage to improve patient outcomes.
- Predicting crises
Machine learning can also be a valuable technique to help predict which patients may be facing a mental health crisis. Patients who have been diagnosed with a mental health condition like bipolar disorder or schizophrenia may seem like they are managing their conditions quite well and then, unexpectedly, enter a manic state or psychosis.
Researchers believe that machine learning techniques may be able to analyze personal data from individual patients, a combination of self-reported data or data passively uploaded from a smart phone or other device, to warn physicians that such a crisis may be imminent.