Mental health professionals and neuroscientists across the globe are now utilizing machine learning to help create better treatment plans for their patients and to recognize essential markers for mental health problems even before they develop. Diane Dreher, Ph.D. says, “The past few years have witnessed an escalation in teen suicides and anxious, depressed, and suicidal students crowding college counseling centers.” That is probably one of the biggest reasons why this form of data science has increasingly risen to popularity – it’s capacity to assist clinicians in predicting individuals who have a higher likelihood of developing a mental health disorder.

There are so many sets of information available to us on mental health, yet we can gather all of this data in a way that mental health professionals can perform their jobs more efficiently. Decades ago, a diagnosis had to be based on statistics, and the overall average population who had the same medical problem. Today, because of machine learning, doctors and other clinicians are capable of personalizing their diagnosis. Machine learning has paved the way for changing the system of mental health through recognizing biomarkers, creating treatment plans, and predicting a crisis.

Recognizing Biomarkers And Creating Treatment Plans

Currently, a trial and error method is used when clinicians diagnose patients with a mental health condition. They need to do this to establish the proper dosage of medicine and to come up with the appropriate treatment plan. This trial and error shouldn’t exist, but then the truth is that one patient’s symptoms may not be present in another patient. Symptoms are almost always different for each patient.

The body does have not only physical biomarkers but also behavioral biomarkers as well for mental issues such as depression and anxiety, among others. And machine learning systems could help recognize these behavioral biomarkers to assist doctors and therapists in determining whether or not a patient is at risk of developing a mental health disorder. The system also helps track the potency of a particular treatment plan.

Thus, it is safe to say that each patient has his biological makeup, reactions, and triggers to stress and other conditions like anxiety or panic. A lot of symptoms for mental health overlap each other, and though some of the key markers are already known, a trial and error regimen shouldn’t be acceptable. Matthew D. Jacofsky, Psy.D. wrote, “To complicate things further, sometimes two separate disorders may be present at the same time. Thus, it is quite possible to have both an eating disorder and an anxiety disorder.”

This is where machine learning comes in. It offers a ready opportunity for mental health professionals to recognize the subcategories of various illnesses and create a more customized treatment plan, including the dosages of the patient’s medications.

Predicting A Crisis

People need to get a good grasp of the reality that those who have specific mental conditions are naturally more inclined to having a crisis like psychosis, panic attacks, or manic episodes. Barbara Markway, Ph.D. explains, “Briefly, a panic attack is a sudden rush of acute fear and anxiety accompanied by physical symptoms such as shortness of breath, dizziness, tightness in the chest, tingling, nausea, and other stomach distress, shaking, and sweating.” Patients who have been diagnosed with a mental health illness are supervised to help them manage their activities of daily living. However, specific conditions like bipolar disorder or schizophrenia have a much higher likelihood of developing a crisis, and the mental healthcare team is accountable for reducing the risk of a crisis from happening.

Machine learning systems can help in this area by combining passive information derived from the media or smartphones and self-provided information to establish if the patient has a forthcoming attack or episode. Crises such as these can be predicted if clinicians have observed the patient experiencing stress or being exposed to his specific triggers.

Conclusion

The bottom line is that there are vivid markers of a forthcoming crisis or episode, and whether or not the patient has a confirmed diagnosis. Each of us has specific triggers as well as coping strategies, and crises can be inevitable. Through clearer biomarkers and more structured treatment plans made possible by machine learning, mental health professionals have a better way of helping their patients recover quickly and effectively.