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AI mental health

Imagine being able to predict the onset of a mental health condition before it hits. Or receive a diagnosis at home instead of a psychiatrist’s office. Or being able to adjust your medications based on patterns in your speech when you casually talk with family and friends.

Within the next five years, you may not have to imagine that at all. According to Guillermo Cecchi, principal research staff member, manager of Computational Psychiatry and Neuroimaging at IBM Research, often-debilitating mental health issues may be much easier to spot and manage, just based on speech and writing alone. According to Cecchi, an approximate minute of speech—thanks to advanced analytics, machine learning, natural-language processing and computational biology—will offer a quantifiable and accurate peek into a patient’s mental health, allowing doctors to more quickly address mental health concerns.

IBM Systems Magazine (ISM): What are the current problems with diagnosing neurological and mental conditions?
Guillermo Cecchi (GC):
Well, there are two main problems. One is the consistency of the diagnosis. If I diagnose you with a certain condition, let’s say depression, a colleague from another institution may have a different diagnosis. Guidelines exist, but very few issues can be quantified in terms that are independent of the background of the patient and the context of the evaluator. The second problem is that even if the evaluation was consistent, it cannot be conducted except in the context of a clinical setting, whether that’s someone coming to a clinic or sending a social worker or psychiatrist to the patient. Bringing the patient to the clinic can cause real problems for some critical conditions, where, for example, they might be scared of being outside or being in the clinic. Sending someone to see the patient can also present a difficult proposition, especially in terms of accessibility. The patient might be in a remote location and the evaluator in the city. It may take several hours to get to the patient. Also, these professionals are chronically overworked, even in places with a relatively high density of treatment providers.

ISM: It’s pretty standard psychiatrists or psychologists sit down with patients and chat with them to help determine a diagnosis. How are you benchmarking against that, if that’s the right word for it?
GC:
We take patients we know have a certain condition as part of the same group of psychiatrists in the same institution and use that as the gold standard to say, ‘Well, whatever we find in the speech patterns that predict the diagnosis is what we’ll consider a marker for the condition.’ In some cases, this is a bit less problematic. We did a study on the predictives of psychotic onset, and because the event itself is so dramatic, it’s hard to mistake it for anything else. No one is saying this is a potential diagnosis. It’s the actual prognosis. We’re still using the gold standard of the psychiatrists, but before the events take place, we actually beat them to the prognosis.

ISM: How do you quantify mental conditions?
GC:
We’re using a combination of machine learning—or what we would call a data-driven or blackbox approach—to try to extract a large number of features to see which have predictive power, and at the same time, we’re trying to formalize ideas that have been around in psychiatry, neurology and psychology for a long time and try to quantify that in speech.

One example is the idea that it’s very important to define psychosis as the “flight of ideas” or incoherence of speech. So the idea that someone who is psychotic may be talking about something and all of a sudden—poof!—goes in a complete different direction, switching topics in an unrelated and inconsistent manner. So that’s something we were able to quantify in certain ways. Of course, this is not an exact science, but we found ways of formalizing that and measuring it in speech that had predictive power. We validated though independent means.

Jim Utsler, IBM Systems Magazine senior writer, has been covering the technology field for more than a decade. Jim can be reached at jjutsler@provide.net.


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