Author: Rob Matheson | MIT News Office<\/p>\n
To diagnose depression, clinicians interview patients, asking specific questions \u2014\u00a0about, say, past mental illnesses, lifestyle, and mood \u2014 and identify the condition based on the patient\u2019s responses.<\/p>\n
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\u2019s 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.<\/p>\n
In a paper being presented at the Interspeech conference, MIT researchers detail 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.<\/p>\n
The researchers hope this method can be used to develop tools to detect signs of depression in natural conversation. In the future, the model could, for instance, power mobile apps that monitor a user\u2019s text and voice for mental distress and send alerts. This could be especially useful for those who can\u2019t get to a clinician for an initial diagnosis, due to distance, cost, or a lack of awareness that something may be wrong.<\/p>\n
\u201cThe first hints we have that a person is happy, excited, sad, or has some serious cognitive condition, such as depression, is through their speech,\u201d says first author Tuka Alhanai, a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL). \u201cIf you want to deploy [depression-detection] models in scalable way \u2026 you want to minimize the amount of constraints you have on the data you\u2019re using. You want to deploy it in any regular conversation and have the model pick up, from the natural interaction, the state of the individual.\u201d<\/p>\n
The technology could still, of course, be used for identifying mental distress in casual conversations in clinical offices, adds co-author James Glass, a senior research scientist in CSAIL. \u201cEvery patient will talk differently, and if the model sees changes maybe it will be a flag to the doctors,\u201d he says. \u201cThis is a step forward in seeing if we can do something assistive to help clinicians.\u201d<\/p>\n
The other co-author on the paper is Mohammad Ghassemi, a member of the Institute for Medical Engineering and Science (IMES).<\/p>\n
Context-free modeling<\/strong><\/p>\n The key innovation of the model lies in its ability to detect patterns indicative of depression, and then map those patterns to new individuals, with no additional information. \u201cWe call it \u2018context-free,\u2019 because you\u2019re not putting any constraints into the types of questions you\u2019re looking for and the type of responses to those questions,\u201d Alhanai says.<\/p>\n Other models are provided with a specific set of questions, and then given examples of how a person without depression responds and examples of how a person with depression responds \u2014 for example, the straightforward inquiry, \u201cDo you have a history of depression?\u201d It uses those exact responses to then determine if a new individual is depressed when asked the exact same question. \u201cBut that\u2019s not how natural conversations work,\u201d Alhanai says. \u00a0\u00a0<\/p>\n The researchers, on the other hand, used a technique called sequence modeling, often used for speech processing. With this technique, they fed the model sequences of text and audio data from questions and answers, from both depressed and non-depressed individuals, one by one. As the sequences accumulated, the model extracted speech patterns that emerged for people with or without depression. Words such as, say, \u201csad,\u201d \u201clow,\u201d or \u201cdown,\u201d may be paired with audio signals that are flatter and more monotone. Individuals with depression may also speak slower and use longer pauses between words. These text and audio identifiers for mental distress have been explored in previous research. It was ultimately up to the model to determine if any patterns were predictive of depression or not.<\/p>\n \u201cThe model sees sequences of words or speaking style, and determines that these patterns are more likely to be seen in people who are depressed or not depressed,\u201d Alhanai says. \u201cThen, if it sees the same sequences in new subjects, it can predict if they\u2019re depressed too.\u201d<\/p>\n This sequencing technique also helps the model look at the conversation as a whole and note differences between how people with and without depression speak over time.<\/p>\n Detecting depression<\/strong><\/p>\n The researchers trained and tested their model on a dataset of 142 interactions from the Distress Analysis Interview Corpus that contains audio, text, and video interviews of patients with mental-health issues and virtual agents controlled by humans. Each subject is rated in terms of depression on a scale between 0 to 27, using the Personal Health Questionnaire. Scores above a cutoff between moderate (10 to 14) and moderately severe (15 to 19) are considered depressed, while all others below that threshold are considered not depressed. Out of all the subjects in the dataset, 28 (20 percent) are labeled as depressed.<\/p>\n In experiments, the model was evaluated using metrics of precision and recall. Precision measures which of the depressed subjects identified by the model were diagnosed as depressed. Recall measures the accuracy of the model in detecting all subjects who were diagnosed as depressed in the entire dataset. In precision, the model scored 71 percent and, on recall, scored 83 percent. The averaged combined score for those metrics, considering any errors, was 77 percent. In the majority of tests, the researchers\u2019 model outperformed nearly all other models.<\/p>\n One key insight from the research, Alhanai notes, is that, during experiments, the model needed much more data to predict depression from audio than text. With text, the model can accurately detect depression using an average of seven question-answer sequences. With audio, the model needed around 30 sequences. \u201cThat implies that the patterns in words people use that are predictive of depression happen in shorter time span in text than in audio,\u201d Alhanai says. Such insights could help the MIT researchers, and others, further refine their models.<\/p>\n This work represents a \u201cvery encouraging\u201d pilot, Glass says. But now the researchers seek to discover what specific patterns the model identifies across scores of raw data. \u201cRight now it\u2019s a bit of a black box,\u201d Glass says. \u201cThese systems, however, are more believable when you have an explanation of what they\u2019re picking up. \u2026 The next challenge is finding out what data it\u2019s seized upon.\u201d<\/p>\n The researchers also aim to test these methods on additional data from many more subjects with other cognitive conditions, such as dementia. \u201cIt\u2019s not so much detecting depression, but it\u2019s a similar concept of evaluating, from an everyday signal in speech, if someone has cognitive impairment or not,\u201d Alhanai says.<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Author: Rob Matheson | MIT News Office To diagnose depression, clinicians interview patients, asking specific questions \u2014\u00a0about, say, past mental illnesses, lifestyle, and mood \u2014 […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":464,"comment_status":"registered_only","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[24],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/983"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=983"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/983\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/472"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=983"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=983"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=983"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}