[T]he notion that artificial intelligence could help predict if a person is suffering from depression is potentially a big step forward—albeit one that brings with it questions about how it might be used.
What makes that possible, says Tuka Alhanai, a researcher at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), is the ability of a machine learning model to identify speech and language patterns associated with depression. More importantly, the model she and fellow MIT scientist Mohammad Ghassemi developed was able to recognize depression with a relatively high degree of accuracy through analyzing how people speak, rather than their specific responses to a clinician’s questions.
It’s what Alhanai refers to as “context-free” analysis; in other words, the model takes its cues from the words people choose and how they say them, without trying to interpret the meaning of their statements.
The potential benefit, Alhanai notes, is that this type of neural network approach could one day be used to evaluate a person’s more natural conversations outside a formal, structured interview with a clinician. That could be helpful in encouraging people to seek professional help when they otherwise might not, due to cost, distance or simply a lack of awareness that something’s wrong.
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