According to the National Institutes of Health, researchers have created a machine learning algorithm called SELSER that analyzes EEG data to determine whether patients may respond well to common antidepressants. The algorithm works by looking for specific neural signals involving complex patterns of brain activity that are associated with positive results from taking the drug.
Clinical depression is a common mental health condition that is difficult to treat. Although there are many different types of antidepressants on the market, the most commonly used antidepressants are SSRI-type drugs, of which sherrin is one of the most popular options. Although some patients responded well to the drug, other patients did not experience an improvement in depressive symptoms and may actually feel unwell after taking the drug.
Scientists have identified a neural signal associated with the positive results of Sherqulin and trained a machine learning algorithm to identify the patient’s signal. The technology could help doctors determine whether prescribing this SSRI prescription is helpful to patients based on their patterns of brain activity.
This is a popular alternative that doctors currently use to determine the best alternative to depression in a particular patient. According to the study, the SELSER algorithm “reliably” predicted participants’ response to Scheklin based on their eemenitagraph information. Similarly, the algorithm can predict “broader clinical outcomes”, not just the extent to which patients respond to such SSRI drugs. For example, the algorithm predicts that patients who respond poorly to shequline are more likely to respond to transcranial magnetic stimulation and psychotherapy.