Machine learning used to successfully predict psychosis

A very interesting study shows that a computer analysis of language use has predicted early signs of future psychosis with ~90% accuracy in at-risk individuals.

,,,results revealed that conversion to psychosis is signaled by low semantic density and talk about voices and sounds. When combined, these two variables were able to predict the conversion with 93% accuracy in the training and 90% accuracy in the holdout datasets. The results point to a larger project in which automated analyses of language are used to forecast a broad range of mental disorders well in advance of their emergence.  (A machine learning approach to predicting psychosis using semantic density and latent content analysis)

An article about the study says:

The results showed that higher than normal usage of words related to sound, combined with a higher rate of using words with similar meaning, meant that psychosis was likely on the horizon. (The whisper of schizophrenia: Machine learning finds ‘sound’ words predict psychosis)

Phillip Wolff, an author of the study, says of it:

“This research is interesting not just for its potential to reveal more about mental illness, but for understanding how the mind works — how it puts ideas together. Machine learning technology is advancing so rapidly that it’s giving us tools to data mine the human mind.” (Ibid)

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