For decades, the “Diagnostic and Statistical Manual of Mental Disorders” (DSM) has been the bible for psychiatric diagnosis. But it’s controversial, with many mental health experts questioning the scientific validity of the data on which it’s based. Continual revisions have not fixed the problem; the DSM-5, published in 2013, is the most controversial yet. Now, a team of researchers, including Rachael Hageman Blair, an assistant professor of biostatistics in UB’s School of Public Health and Health Professions, has received an NSF grant to investigate whether big data can be used to develop a more rigorous approach to classifying mood disorders—one that, hopefully, everyone can agree on.
There is no biological marker. The diagnosis is based on a semi-structured interview, which consists of various modules related to different disorders. There are other tools like a “Mood Disorder Diagnostic Questionnaire,” which is based on patient self-reporting.
The National Institute of Mental Health noted recently that treatment for mood disorders is effective in less than 25 percent of patients.
The symptoms overlap considerably. And they’re very elusive, arising from different biological, psychosocial and genetic factors.
We’re taking first steps to cluster patients with respect to some of the features known to drive mood disorders. It’s challenging, because there’s uncertainty everywhere. There’s uncertainty in the data, especially self-reporting data. It’s also not clear what features contribute to the disorder, so there’s a variable selection problem. And the labels of the disorders themselves—such as unipolar and bipolar—are loaded with uncertainty that comes from doctor bias and the ambiguity of the DSM. We’re developing new methods to overcome some of these challenges.
We have a tremendous amount of data that ranges from MRI images to genetic profiles, clinical measures, information about childhood, finances, diet and many self-reporting-style questions on mood.
Our long-term goal is to develop interactive tools that can be used by a clinician to help categorize patients, and identify those who would benefit from certain drugs.
There are lots of things, because existing methods simply “break” under the load. More work needs to be done at a fundamental level to fully realize what big data has to offer.