Detecting clinically significant events through automated language analysis: Quo imus?

Abstract

We found the recent paper by Bedi et al. 1 simultaneously exciting, heartening and, sadly, a bit discouraging. It shows that modern, statistical natural language processing (NLP) and machinelearning (ML) techniques can potentially be useful as a component of diagnosis, here predicting who among those at risk will eventually transition to full-blown psychosis. This result follows closely our own and others observations of the value of these techniques in, for example, discriminating patients with schizophrenia from controls, 2 discriminating schizophrenia probands, first-degree relatives and unrelated healthy controls, 3 differentiating those at high risk of psychosis from unrelated putatively healthy participants 4 and in a candidate gene study linking language in general to underlying neurobiology, 5 all quite encouraging outcomes.

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