• Hypertrophic Cardiomyopathy (HCM)
  • Vaccines: 2023 Year in Review
  • Eyecare
  • Urothelial Carcinoma
  • Women's Health
  • Hemophilia
  • Heart Failure
  • Vaccines
  • Neonatal Care
  • Type II Inflammation
  • Substance Use Disorder
  • Gene Therapy
  • Lung Cancer
  • Spinal Muscular Atrophy
  • HIV
  • Post-Acute Care
  • Liver Disease
  • Pulmonary Arterial Hypertension
  • Biologics
  • Asthma
  • Atrial Fibrillation
  • Type I Diabetes
  • RSV
  • COVID-19
  • Cardiovascular Diseases
  • Breast Cancer
  • Prescription Digital Therapeutics
  • Reproductive Health
  • The Improving Patient Access Podcast
  • Blood Cancer
  • Ulcerative Colitis
  • Respiratory Conditions
  • Multiple Sclerosis
  • Digital Health
  • Population Health
  • Sleep Disorders
  • Biosimilars
  • Plaque Psoriasis
  • Leukemia and Lymphoma
  • Oncology
  • Pediatrics
  • Urology
  • Obstetrics-Gynecology & Women's Health
  • Opioids
  • Solid Tumors
  • Autoimmune Diseases
  • Dermatology
  • Diabetes
  • Mental Health

How Machine Learning is Changing Mental Health


Machine learning is helping scientists better understand the development of mental health conditions-and the best ways to help treat them.

AI machine learning

It almost sounds like a science fiction movie.

Artificially-intelligent computer algorithms take in vast amounts of data about behavior and the brain, without being explicitly programmed, to “learn” about specific mental health conditions-helping to predict who may be at risk for a particular disorder or predict what treatment a patient who has already been diagnosed with a mental health condition will best respond to.

But although it may sound like something from an Isaac Asimov short story, it’s not. This technique is called “machine learning,” and neuroscientists and clinicians across the globe are now using these special algorithms to help make reliable predictions about treatment outcomes.

“Advances in the field of computer science are offering use new tools to understand the complex activities of the nervous system,” says Chethan Pandarinath, PhD, a biomedical engineer who uses machine learning to help develop assistive devices for people with neurological disorders at Emory University. “These tools are helping us to understand the brain better, and what we can do to make improvements when we need to.”

David Benrimoh, MD, CM, a psychiatry resident at McGill University says that machine learning also offers another important advantage when looking at mental health disorders.

“Machine learning really meets a specific need that we have in psychiatry-and that’s the need for personalization,” he says. “For decades, we’ve been working on group averages and statistics that apply to populations who may have the same diagnosis but don’t translate as well to an individual patient. Machine learning allows us to get at individual predictions in a way we haven’t been able to before.”

Here are two ways that machine learning is helping to change the face of mental health:

1. 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.

Related article: Four Ways Health Execs Can Help ‘Cure Stigma’ of Mental Health

If machine learning algorithms could better determine important biomarkers, psychiatrists would be in better position to determine who is at risk of developing a particular mental health disorder, as well as to choose and then track the progress of a particular intervention.

“These algorithms can identify patterns that can help us cluster patients on markers outside of what we currently do-cluster based on severity or specific symptoms,” says Benrimoh. “By looking at these different patterns, and who responds to what treatment, we can do a better job of determining relevant sub-types of different disorders and which treatments are most effective to deal with them.”

2. 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 condition 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.

“By layering the technology to take in a variety of different information about a patient, you can develop something that could be quite useful,” says Benmiroh.

While both Pandarinath and Benrimoh are very optimistic about the future uses of machine learning to help treat a variety of mental health conditions, Benrimoh says it’s important to understand what machine learning really is-what it can offer and where it may be limited.

“At the end of it all, machine learning really is just a good mathematical model that can offer individual model prediction,” he says. “It’s a tool that can help mental health providers that much better at their jobs by identifying patterns that they may not notice-and, in doing so, ultimately offer the best quality care to their patients.

Kayt Sukel is a science and health writer based outside Houston.

© 2024 MJH Life Sciences

All rights reserved.