MIT researchers teach a neural network to recognize depression

<p>A new technology by <strong>MIT </strong>researchers can <strong>sense depression</strong> by analyzing the written and spoken responses by a patient. The system, pioneered by <strong>MIT&rsquo;s CSAIL group, </strong>uses &ldquo;a neural-network model that can be unleashed on raw text and audio data from interviews to discover speech patterns indicative of depression.&rdquo;</p> <p>&ldquo;Given a new subject, it can accurately predict if the individual is depressed, without needing any other information about the questions and answers,&rdquo; the researchers write.</p> <p>The most important part of the system is that it is context-free. This means that it doesn&rsquo;t require specific questions or types of responses. It simply uses day-to-day interactions as the source data.</p> <p>&ldquo;We call it &lsquo;context-free,&rsquo; because you&rsquo;re not putting any constraints into the types of questions you&rsquo;re looking for and the type of responses to those questions,&rdquo; said researcher<strong> Tuka Alhanai.</strong></p> <p>&ldquo;Every patient will talk differently, and if the model sees changes maybe it will be a flag to the doctors,&rdquo; said study co-author James Glass. &ldquo;This is a step forward in seeing if we can do something assistive to help clinicians.&rdquo;</p> <p>Obviously detection is only part of the process but this robo-therapist could help real therapists find and isolate issues automatically versus the long process of analysis. It&rsquo;s a fascinating step forward in mental health.</p>