To diagnose depression, clinicians interview patients, asking specific questions — about, say, past mental illnesses, lifestyle, and mood — and identify the condition based on the patient’s responses.
In recent years, machine learning has been championed as a useful aid for diagnostics. Machine-learning models, for instance, have been developed that can detect words and intonations of speech that may indicate depression.
But these models tend to predict that a person is depressed or not, based on the person’s specific answers to specific questions. These methods are accurate, but their reliance on the type of question being asked limits how and where they can be used.
In a paper recently presented at the Interspeech conference in Hyderabad, India, MIT researchers detailed a neural-network model that can be unleashed on raw text and audio data from interviews to discover speech patterns indicative of depression.
Given a new subject, it can accurately predict if the individual is depressed, without needing any other information about the questions and answers.