Aug. 7, 2018
A clinical decision support tool to differentiate mental illness and appropriate medication.
This predictive algorithm has demonstrated high accuracy (>90%) in tests using medication-class responses in real patients. The most significant advantage of this approach is that it utilizes objective criteria, namely, brain activity, rather than subjective evaluations. This reduces hypothesis bias, allowing the data to draw its own conclusions.
Differentiating bipolar disorder (BD) from major depressive disorder (MDD) using current methods such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) is challenging in patients without obvious distinctions. The DSM contains a predefined set of criteria used to diagnose a patient and does not always provide a clear diagnosis. Because patients with BD generally spend more time in depressive than manic states, BD is often misdiagnosed as MDD. BD patients often go 6–10 years without a proper diagnosis. While the adoption of a spectrum approach to mood disorder diagnosis is appealing, the treatment response of patients to the different medication classes, antidepressants (AD) versus mood stabilizers (MS), suggests that there is a fundamental difference between MDD and BD in many cases. MSs often do not effectively treat MDD, while ADs may worsen BD type I. Obtaining the correct mood diagnosis and the correct medication-class to best support recovery of a patient is imperative.
Researchers at the Mind Research Network and the University of New Mexico have developed a clinical decision support tool to differentiate mental illness and appropriate medication. This predictive algorithm has demonstrated high accuracy (>90%) in tests using medication-class responses in real patients. The most significant advantage of this approach is that it utilizes objective criteria, namely, brain activity, rather than subjective evaluations. This reduces hypothesis bias, allowing the data to draw its own conclusions.
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demonstrated high accuracy
reduces hypothesis bias
versus mood stabilizers
technology description researchers
mind research network
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